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detection</title> <meta name="description" content="Search results for: Intrusion detection"> <meta name="keywords" content="Intrusion detection"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Intrusion detection"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 1553</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Intrusion detection</h1> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1553</span> Multisensor Agent Based Intrusion Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Richard%20A.%20Wasniowski">Richard A. Wasniowski</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we propose a framework for multisensor intrusion detection called Fuzzy Agent-Based Intrusion Detection System. A unique feature of this model is that the agent uses data from multiple sensors and the fuzzy logic to process log files. Use of this feature reduces the overhead in a distributed intrusion detection system. We have developed an agent communication architecture that provides a prototype implementation. This paper discusses also the issues of combining intelligent agent technology with the intrusion detection domain. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Intrusion%20detection" title="Intrusion detection">Intrusion detection</a>, <a href="https://publications.waset.org/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a>, <a href="https://publications.waset.org/search?q=agents" title=" agents"> agents</a>, <a href="https://publications.waset.org/search?q=networksecurity." title=" networksecurity."> networksecurity.</a> </p> <a href="https://publications.waset.org/2889/multisensor-agent-based-intrusion-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/2889/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/2889/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/2889/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/2889/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/2889/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/2889/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/2889/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/2889/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/2889/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/2889/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/2889.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1919</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1552</span> Network Intrusion Detection Design Using Feature Selection of Soft Computing Paradigms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=T.%20S.%20Chou">T. S. Chou</a>, <a href="https://publications.waset.org/search?q=K.%20K.%20Yen"> K. K. Yen</a>, <a href="https://publications.waset.org/search?q=J.%20Luo"> J. Luo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The network traffic data provided for the design of intrusion detection always are large with ineffective information and enclose limited and ambiguous information about users- activities. We study the problems and propose a two phases approach in our intrusion detection design. In the first phase, we develop a correlation-based feature selection algorithm to remove the worthless information from the original high dimensional database. Next, we design an intrusion detection method to solve the problems of uncertainty caused by limited and ambiguous information. In the experiments, we choose six UCI databases and DARPA KDD99 intrusion detection data set as our evaluation tools. Empirical studies indicate that our feature selection algorithm is capable of reducing the size of data set. Our intrusion detection method achieves a better performance than those of participating intrusion detectors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Intrusion%20detection" title="Intrusion detection">Intrusion detection</a>, <a href="https://publications.waset.org/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/search?q=k-nearest%0Aneighbors" title=" k-nearest neighbors"> k-nearest neighbors</a>, <a href="https://publications.waset.org/search?q=fuzzy%20clustering" title=" fuzzy clustering"> fuzzy clustering</a>, <a href="https://publications.waset.org/search?q=Dempster-Shafer%20theory" title=" Dempster-Shafer theory"> Dempster-Shafer theory</a> </p> <a href="https://publications.waset.org/3936/network-intrusion-detection-design-using-feature-selection-of-soft-computing-paradigms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/3936/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/3936/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/3936/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/3936/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/3936/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/3936/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/3936/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/3936/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/3936/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/3936/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/3936.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1933</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1551</span> A Novel Hybrid Mobile Agent Based Distributed Intrusion Detection System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Amir%20Vahid%20Dastjerdi">Amir Vahid Dastjerdi</a>, <a href="https://publications.waset.org/search?q=Kamalrulnizam%20Abu%20Bakar"> Kamalrulnizam Abu Bakar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The first generation of Mobile Agents based Intrusion Detection System just had two components namely data collection and single centralized analyzer. The disadvantage of this type of intrusion detection is if connection to the analyzer fails, the entire system will become useless. In this work, we propose novel hybrid model for Mobile Agent based Distributed Intrusion Detection System to overcome the current problem. The proposed model has new features such as robustness, capability of detecting intrusion against the IDS itself and capability of updating itself to detect new pattern of intrusions. In addition, our proposed model is also capable of tackling some of the weaknesses of centralized Intrusion Detection System models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Distributed%20Intrusion%20Detection%20System" title="Distributed Intrusion Detection System">Distributed Intrusion Detection System</a>, <a href="https://publications.waset.org/search?q=Mobile%0AAgents" title=" Mobile Agents"> Mobile Agents</a>, <a href="https://publications.waset.org/search?q=Network%20Security." title=" Network Security."> Network Security.</a> </p> <a href="https://publications.waset.org/3059/a-novel-hybrid-mobile-agent-based-distributed-intrusion-detection-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/3059/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/3059/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/3059/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/3059/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/3059/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/3059/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/3059/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/3059/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/3059/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/3059/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/3059.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1781</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1550</span> Investigating Intrusion Detection Systems in MANET and Comparing IDSs for Detecting Misbehaving Nodes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Marjan%20Kuchaki%20Rafsanjani">Marjan Kuchaki Rafsanjani</a>, <a href="https://publications.waset.org/search?q=Ali%20Movaghar"> Ali Movaghar</a>, <a href="https://publications.waset.org/search?q=Faroukh%20Koroupi"> Faroukh Koroupi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As mobile ad hoc networks (MANET) have different characteristics from wired networks and even from standard wireless networks, there are new challenges related to security issues that need to be addressed. Due to its unique features such as open nature, lack of infrastructure and central management, node mobility and change of dynamic topology, prevention methods from attacks on them are not enough. Therefore intrusion detection is one of the possible ways in recognizing a possible attack before the system could be penetrated. All in all, techniques for intrusion detection in old wireless networks are not suitable for MANET. In this paper, we classify the architecture for Intrusion detection systems that have so far been introduced for MANETs, and then existing intrusion detection techniques in MANET presented and compared. We then indicate important future research directions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Intrusion%20Detection%20System%28IDS%29" title="Intrusion Detection System(IDS)">Intrusion Detection System(IDS)</a>, <a href="https://publications.waset.org/search?q=Misbehavingnodes" title=" Misbehavingnodes"> Misbehavingnodes</a>, <a href="https://publications.waset.org/search?q=Mobile%20Ad%20Hoc%20Network%28MANET%29" title=" Mobile Ad Hoc Network(MANET)"> Mobile Ad Hoc Network(MANET)</a>, <a href="https://publications.waset.org/search?q=Security." title=" Security."> Security.</a> </p> <a href="https://publications.waset.org/6483/investigating-intrusion-detection-systems-in-manet-and-comparing-idss-for-detecting-misbehaving-nodes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/6483/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/6483/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/6483/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/6483/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/6483/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/6483/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/6483/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/6483/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/6483/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/6483/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/6483.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2025</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1549</span> Intrusion Detection based on Distance Combination</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Joffroy%20Beauquier">Joffroy Beauquier</a>, <a href="https://publications.waset.org/search?q=Yongjie%20Hu"> Yongjie Hu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>The intrusion detection problem has been frequently studied, but intrusion detection methods are often based on a single point of view, which always limits the results. In this paper, we introduce a new intrusion detection model based on the combination of different current methods. First we use a notion of distance to unify the different methods. Second we combine these methods using the Pearson correlation coefficients, which measure the relationship between two methods, and we obtain a combined distance. If the combined distance is greater than a predetermined threshold, an intrusion is detected. We have implemented and tested the combination model with two different public data sets: the data set of masquerade detection collected by Schonlau & al., and the data set of program behaviors from the University of New Mexico. The results of the experiments prove that the combination model has better performances.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Intrusion%20detection" title="Intrusion detection">Intrusion detection</a>, <a href="https://publications.waset.org/search?q=combination" title=" combination"> combination</a>, <a href="https://publications.waset.org/search?q=distance" title=" distance"> distance</a>, <a href="https://publications.waset.org/search?q=Pearson%20correlation%20coefficients." title=" Pearson correlation coefficients."> Pearson correlation coefficients.</a> </p> <a href="https://publications.waset.org/13410/intrusion-detection-based-on-distance-combination" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/13410/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/13410/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/13410/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/13410/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/13410/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/13410/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/13410/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/13410/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/13410/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/13410/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/13410.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1842</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1548</span> Improved C-Fuzzy Decision Tree for Intrusion Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Krishnamoorthi%20Makkithaya">Krishnamoorthi Makkithaya</a>, <a href="https://publications.waset.org/search?q=N.%20V.%20Subba%20Reddy"> N. V. Subba Reddy</a>, <a href="https://publications.waset.org/search?q=U.%20Dinesh%20Acharya"> U. Dinesh Acharya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As the number of networked computers grows, intrusion detection is an essential component in keeping networks secure. Various approaches for intrusion detection are currently being in use with each one has its own merits and demerits. This paper presents our work to test and improve the performance of a new class of decision tree c-fuzzy decision tree to detect intrusion. The work also includes identifying best candidate feature sub set to build the efficient c-fuzzy decision tree based Intrusion Detection System (IDS). We investigated the usefulness of c-fuzzy decision tree for developing IDS with a data partition based on horizontal fragmentation. Empirical results indicate the usefulness of our approach in developing the efficient IDS. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Data%20mining" title="Data mining">Data mining</a>, <a href="https://publications.waset.org/search?q=Decision%20tree" title=" Decision tree"> Decision tree</a>, <a href="https://publications.waset.org/search?q=Feature%20selection" title=" Feature selection"> Feature selection</a>, <a href="https://publications.waset.org/search?q=Fuzzyc-%20means%20clustering" title=" Fuzzyc- means clustering"> Fuzzyc- means clustering</a>, <a href="https://publications.waset.org/search?q=Intrusion%20detection." title=" Intrusion detection."> Intrusion detection.</a> </p> <a href="https://publications.waset.org/15151/improved-c-fuzzy-decision-tree-for-intrusion-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/15151/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/15151/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/15151/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/15151/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/15151/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/15151/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/15151/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/15151/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/15151/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/15151/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/15151.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1576</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1547</span> Intrusion Detection System Based On The Integrity of TCP Packet</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Moad%20Alhamaty">Moad Alhamaty </a>, <a href="https://publications.waset.org/search?q=Ali%20Yazdian"> Ali Yazdian </a>, <a href="https://publications.waset.org/search?q=Fathi%20Al-qadasi"> Fathi Al-qadasi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>A common way to elude the signature-based Network Intrusion Detection System is based upon changing a recognizable attack to an unrecognizable one via the IDS. For example, in order to evade sign accommodation with intrusion detection system markers, a hacker spilt the payload packet into many small pieces or hides them within messages. In this paper we try to model the main fragmentation attack and create a new module in the intrusion detection architecture system which recognizes the main fragmentation attacks through verification of integrity checking of TCP packet in order to prevent elusion of the system and also to announce the necessary alert to the system administrator.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Intrusion%20detection%20system" title="Intrusion detection system">Intrusion detection system</a>, <a href="https://publications.waset.org/search?q=Evasion%20techniques" title=" Evasion techniques"> Evasion techniques</a>, <a href="https://publications.waset.org/search?q=Fragmentation%20attacks" title=" Fragmentation attacks"> Fragmentation attacks</a>, <a href="https://publications.waset.org/search?q=TCP%20Packet%20integrity." title=" TCP Packet integrity."> TCP Packet integrity.</a> </p> <a href="https://publications.waset.org/3738/intrusion-detection-system-based-on-the-integrity-of-tcp-packet" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/3738/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/3738/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/3738/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/3738/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/3738/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/3738/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/3738/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/3738/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/3738/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/3738/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/3738.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1850</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1546</span> Adaptive Network Intrusion Detection Learning: Attribute Selection and Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Dewan%20Md.%20Farid">Dewan Md. Farid</a>, <a href="https://publications.waset.org/search?q=Jerome%20Darmont"> Jerome Darmont</a>, <a href="https://publications.waset.org/search?q=Nouria%20Harbi"> Nouria Harbi</a>, <a href="https://publications.waset.org/search?q=Nguyen%20Huu%20Hoa"> Nguyen Huu Hoa</a>, <a href="https://publications.waset.org/search?q=Mohammad%20Zahidur%20Rahman"> Mohammad Zahidur Rahman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a new learning approach for network intrusion detection using na茂ve Bayesian classifier and ID3 algorithm is presented, which identifies effective attributes from the training dataset, calculates the conditional probabilities for the best attribute values, and then correctly classifies all the examples of training and testing dataset. Most of the current intrusion detection datasets are dynamic, complex and contain large number of attributes. Some of the attributes may be redundant or contribute little for detection making. It has been successfully tested that significant attribute selection is important to design a real world intrusion detection systems (IDS). The purpose of this study is to identify effective attributes from the training dataset to build a classifier for network intrusion detection using data mining algorithms. The experimental results on KDD99 benchmark intrusion detection dataset demonstrate that this new approach achieves high classification rates and reduce false positives using limited computational resources. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Attributes%20selection" title="Attributes selection">Attributes selection</a>, <a href="https://publications.waset.org/search?q=Conditional%20probabilities" title=" Conditional probabilities"> Conditional probabilities</a>, <a href="https://publications.waset.org/search?q=information%20gain" title="information gain">information gain</a>, <a href="https://publications.waset.org/search?q=network%20intrusion%20detection." title=" network intrusion detection."> network intrusion detection.</a> </p> <a href="https://publications.waset.org/6516/adaptive-network-intrusion-detection-learning-attribute-selection-and-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/6516/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/6516/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/6516/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/6516/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/6516/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/6516/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/6516/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/6516/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/6516/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/6516/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/6516.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2698</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1545</span> Research on Hybrid Neural Network in Intrusion Detection System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Jianhua%20Wang">Jianhua Wang</a>, <a href="https://publications.waset.org/search?q=Yan%20Yu"> Yan Yu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>This paper presents an intrusion detection system of hybrid neural network model based on RBF and Elman. It is used for anomaly detection and misuse detection. This model has the memory function .It can detect discrete and related aggressive behavior effectively. RBF network is a real-time pattern classifier, and Elman network achieves the memory ability for former event. Based on the hybrid model intrusion detection system uses DARPA data set to do test evaluation. It uses ROC curve to display the test result intuitively. After the experiment it proves this hybrid model intrusion detection system can effectively improve the detection rate, and reduce the rate of false alarm and fail.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=RBF" title="RBF">RBF</a>, <a href="https://publications.waset.org/search?q=Elman" title=" Elman"> Elman</a>, <a href="https://publications.waset.org/search?q=anomaly%20detection" title=" anomaly detection"> anomaly detection</a>, <a href="https://publications.waset.org/search?q=misuse%20detection" title=" misuse detection"> misuse detection</a>, <a href="https://publications.waset.org/search?q=hybrid%20neural%20network." title=" hybrid neural network."> hybrid neural network.</a> </p> <a href="https://publications.waset.org/7003/research-on-hybrid-neural-network-in-intrusion-detection-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/7003/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/7003/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/7003/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/7003/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/7003/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/7003/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/7003/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/7003/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/7003/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/7003/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/7003.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2327</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1544</span> Key Issues and Challenges of Intrusion Detection and Prevention System: Developing Proactive Protection in Wireless Network Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=M.%20Salman">M. Salman</a>, <a href="https://publications.waset.org/search?q=B.%20Budiardjo"> B. Budiardjo</a>, <a href="https://publications.waset.org/search?q=K.%20Ramli"> K. Ramli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays wireless technology plays an important role in public and personal communication. However, the growth of wireless networking has confused the traditional boundaries between trusted and untrusted networks. Wireless networks are subject to a variety of threats and attacks at present. An attacker has the ability to listen to all network traffic which becoming a potential intrusion. Intrusion of any kind may lead to a chaotic condition. In addition, improperly configured access points also contribute the risk to wireless network. To overcome this issue, a security solution that includes an intrusion detection and prevention system need to be implemented. In this paper, first the security drawbacks of wireless network will be analyzed then investigate the characteristics and also the limitations on current wireless intrusion detection and prevention system. Finally, the requirement of next wireless intrusion prevention system will be identified including some key issues which should be focused on in the future to overcomes those limitations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=intrusion%20detection" title="intrusion detection">intrusion detection</a>, <a href="https://publications.waset.org/search?q=intrusion%20prevention" title=" intrusion prevention"> intrusion prevention</a>, <a href="https://publications.waset.org/search?q=wireless%0Anetworks" title=" wireless networks"> wireless networks</a>, <a href="https://publications.waset.org/search?q=proactive%20protection" title=" proactive protection"> proactive protection</a> </p> <a href="https://publications.waset.org/2096/key-issues-and-challenges-of-intrusion-detection-and-prevention-system-developing-proactive-protection-in-wireless-network-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/2096/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/2096/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/2096/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/2096/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/2096/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/2096/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/2096/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/2096/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/2096/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/2096/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/2096.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">3938</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1543</span> A Review on Soft Computing Technique in Intrusion Detection System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Noor%20Suhana%20Sulaiman">Noor Suhana Sulaiman</a>, <a href="https://publications.waset.org/search?q=Rohani%20Abu%20Bakar"> Rohani Abu Bakar</a>, <a href="https://publications.waset.org/search?q=Norrozila%20Sulaiman"> Norrozila Sulaiman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Intrusion Detection System is significant in network security. It detects and identifies intrusion behavior or intrusion attempts in a computer system by monitoring and analyzing the network packets in real time. In the recent year, intelligent algorithms applied in the intrusion detection system (IDS) have been an increasing concern with the rapid growth of the network security. IDS data deals with a huge amount of data which contains irrelevant and redundant features causing slow training and testing process, higher resource consumption as well as poor detection rate. Since the amount of audit data that an IDS needs to examine is very large even for a small network, classification by hand is impossible. Hence, the primary objective of this review is to review the techniques prior to classification process suit to IDS data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Intrusion%20Detection%20System" title="Intrusion Detection System">Intrusion Detection System</a>, <a href="https://publications.waset.org/search?q=security" title=" security"> security</a>, <a href="https://publications.waset.org/search?q=soft%0Acomputing" title=" soft computing"> soft computing</a>, <a href="https://publications.waset.org/search?q=classification." title=" classification."> classification.</a> </p> <a href="https://publications.waset.org/13529/a-review-on-soft-computing-technique-in-intrusion-detection-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/13529/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/13529/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/13529/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/13529/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/13529/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/13529/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/13529/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/13529/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/13529/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/13529/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/13529.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1864</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1542</span> Hybrid Intelligent Intrusion Detection System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Norbik%20Bashah">Norbik Bashah</a>, <a href="https://publications.waset.org/search?q=Idris%20Bharanidharan%20Shanmugam"> Idris Bharanidharan Shanmugam</a>, <a href="https://publications.waset.org/search?q=Abdul%20Manan%20Ahmed"> Abdul Manan Ahmed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Intrusion Detection Systems are increasingly a key part of systems defense. Various approaches to Intrusion Detection are currently being used, but they are relatively ineffective. Artificial Intelligence plays a driving role in security services. This paper proposes a dynamic model Intelligent Intrusion Detection System, based on specific AI approach for intrusion detection. The techniques that are being investigated includes neural networks and fuzzy logic with network profiling, that uses simple data mining techniques to process the network data. The proposed system is a hybrid system that combines anomaly, misuse and host based detection. Simple Fuzzy rules allow us to construct if-then rules that reflect common ways of describing security attacks. For host based intrusion detection we use neural-networks along with self organizing maps. Suspicious intrusions can be traced back to its original source path and any traffic from that particular source will be redirected back to them in future. Both network traffic and system audit data are used as inputs for both. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Intrusion%20Detection" title="Intrusion Detection">Intrusion Detection</a>, <a href="https://publications.waset.org/search?q=Network%20Security" title=" Network Security"> Network Security</a>, <a href="https://publications.waset.org/search?q=Data%20mining" title=" Data mining"> Data mining</a>, <a href="https://publications.waset.org/search?q=Fuzzy%20Logic." title=" Fuzzy Logic."> Fuzzy Logic.</a> </p> <a href="https://publications.waset.org/5552/hybrid-intelligent-intrusion-detection-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/5552/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/5552/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/5552/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/5552/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/5552/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/5552/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/5552/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/5552/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/5552/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/5552/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/5552.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2131</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1541</span> Scaling up Detection Rates and Reducing False Positives in Intrusion Detection using NBTree</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Dewan%20Md.%20Farid">Dewan Md. Farid</a>, <a href="https://publications.waset.org/search?q=Nguyen%20Huu%20Hoa"> Nguyen Huu Hoa</a>, <a href="https://publications.waset.org/search?q=Jerome%20Darmont"> Jerome Darmont</a>, <a href="https://publications.waset.org/search?q=Nouria%20Harbi"> Nouria Harbi</a>, <a href="https://publications.waset.org/search?q=Mohammad%20Zahidur%20Rahman"> Mohammad Zahidur Rahman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a new learning algorithm for anomaly based network intrusion detection using improved self adaptive na茂ve Bayesian tree (NBTree), which induces a hybrid of decision tree and na茂ve Bayesian classifier. The proposed approach scales up the balance detections for different attack types and keeps the false positives at acceptable level in intrusion detection. In complex and dynamic large intrusion detection dataset, the detection accuracy of na茂ve Bayesian classifier does not scale up as well as decision tree. It has been successfully tested in other problem domains that na茂ve Bayesian tree improves the classification rates in large dataset. In na茂ve Bayesian tree nodes contain and split as regular decision-trees, but the leaves contain na茂ve Bayesian classifiers. The experimental results on KDD99 benchmark network intrusion detection dataset demonstrate that this new approach scales up the detection rates for different attack types and reduces false positives in network intrusion detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Detection%20rates" title="Detection rates">Detection rates</a>, <a href="https://publications.waset.org/search?q=false%20positives" title=" false positives"> false positives</a>, <a href="https://publications.waset.org/search?q=network%20intrusiondetection" title=" network intrusiondetection"> network intrusiondetection</a>, <a href="https://publications.waset.org/search?q=na%C3%AFve%20Bayesian%20tree." title=" na茂ve Bayesian tree."> na茂ve Bayesian tree.</a> </p> <a href="https://publications.waset.org/1750/scaling-up-detection-rates-and-reducing-false-positives-in-intrusion-detection-using-nbtree" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/1750/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/1750/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/1750/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/1750/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/1750/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/1750/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/1750/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/1750/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/1750/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/1750/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/1750.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2281</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1540</span> Attacks Classification in Adaptive Intrusion Detection using Decision Tree</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Dewan%20Md.%20Farid">Dewan Md. Farid</a>, <a href="https://publications.waset.org/search?q=Nouria%20Harbi"> Nouria Harbi</a>, <a href="https://publications.waset.org/search?q=Emna%20Bahri"> Emna Bahri</a>, <a href="https://publications.waset.org/search?q=Mohammad%20Zahidur%20Rahman"> Mohammad Zahidur Rahman</a>, <a href="https://publications.waset.org/search?q=Chowdhury%20Mofizur%20Rahman"> Chowdhury Mofizur Rahman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, information security has become a key issue in information technology as the number of computer security breaches are exposed to an increasing number of security threats. A variety of intrusion detection systems (IDS) have been employed for protecting computers and networks from malicious network-based or host-based attacks by using traditional statistical methods to new data mining approaches in last decades. However, today's commercially available intrusion detection systems are signature-based that are not capable of detecting unknown attacks. In this paper, we present a new learning algorithm for anomaly based network intrusion detection system using decision tree algorithm that distinguishes attacks from normal behaviors and identifies different types of intrusions. Experimental results on the KDD99 benchmark network intrusion detection dataset demonstrate that the proposed learning algorithm achieved 98% detection rate (DR) in comparison with other existing methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Detection%20rate" title="Detection rate">Detection rate</a>, <a href="https://publications.waset.org/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/search?q=intrusion%20detectionsystem" title=" intrusion detectionsystem"> intrusion detectionsystem</a>, <a href="https://publications.waset.org/search?q=network%20security." title=" network security."> network security.</a> </p> <a href="https://publications.waset.org/5652/attacks-classification-in-adaptive-intrusion-detection-using-decision-tree" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/5652/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/5652/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/5652/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/5652/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/5652/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/5652/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/5652/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/5652/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/5652/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/5652/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/5652.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">3630</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1539</span> Detection of New Attacks on Ubiquitous Services in Cloud Computing and Countermeasures </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=L.%20Sellami">L. Sellami</a>, <a href="https://publications.waset.org/search?q=D.%20Idoughi"> D. Idoughi</a>, <a href="https://publications.waset.org/search?q=P.%20F.%20Tiako"> P. F. Tiako</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Cloud computing provides infrastructure to the enterprise through the Internet allowing access to cloud services at anytime and anywhere. This pervasive aspect of the services, the distributed nature of data and the wide use of information make cloud computing vulnerable to intrusions that violate the security of the cloud. This requires the use of security mechanisms to detect malicious behavior in network communications and hosts such as intrusion detection systems (IDS). In this article, we focus on the detection of intrusion into the cloud sing IDSs. We base ourselves on client authentication in the computing cloud. This technique allows to detect the abnormal use of ubiquitous service and prevents the intrusion of cloud computing. This is an approach based on client authentication data. Our IDS provides intrusion detection inside and outside cloud computing network. It is a double protection approach: The security user node and the global security cloud computing.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Cloud%20computing" title="Cloud computing">Cloud computing</a>, <a href="https://publications.waset.org/search?q=intrusion%20detection%20system" title=" intrusion detection system"> intrusion detection system</a>, <a href="https://publications.waset.org/search?q=privacy" title=" privacy"> privacy</a>, <a href="https://publications.waset.org/search?q=trust." title=" trust."> trust.</a> </p> <a href="https://publications.waset.org/10006735/detection-of-new-attacks-on-ubiquitous-services-in-cloud-computing-and-countermeasures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10006735/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10006735/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10006735/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10006735/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10006735/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10006735/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10006735/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10006735/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10006735/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10006735/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10006735.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1099</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1538</span> An Edit-Distance Algorithm to Detect Correlated Attacks in Distributed Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Sule%20Simsek">Sule Simsek</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Intrusion detection systems (IDS)are crucial components of the security mechanisms of today-s computer systems. Existing research on intrusion detection has focused on sequential intrusions. However, intrusions can also be formed by concurrent interactions of multiple processes. Some of the intrusions caused by these interactions cannot be detected using sequential intrusion detection methods. Therefore, there is a need for a mechanism that views the distributed system as a whole. L-BIDS (Lattice-Based Intrusion Detection System) is proposed to address this problem. In the L-BIDS framework, a library of intrusions and distributed traces are represented as lattices. Then these lattices are compared in order to detect intrusions in the distributed traces. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Attack%20graph" title="Attack graph">Attack graph</a>, <a href="https://publications.waset.org/search?q=distributed" title=" distributed"> distributed</a>, <a href="https://publications.waset.org/search?q=edit-distance" title=" edit-distance"> edit-distance</a>, <a href="https://publications.waset.org/search?q=misuse%20detection." title=" misuse detection."> misuse detection.</a> </p> <a href="https://publications.waset.org/15419/an-edit-distance-algorithm-to-detect-correlated-attacks-in-distributed-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/15419/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/15419/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/15419/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/15419/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/15419/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/15419/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/15419/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/15419/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/15419/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/15419/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/15419.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1388</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1537</span> Unsupervised Clustering Methods for Identifying Rare Events in Anomaly Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Witcha%20Chimphlee">Witcha Chimphlee</a>, <a href="https://publications.waset.org/search?q=Abdul%20Hanan%20Abdullah"> Abdul Hanan Abdullah</a>, <a href="https://publications.waset.org/search?q=Mohd%20Noor%20Md%20Sap"> Mohd Noor Md Sap</a>, <a href="https://publications.waset.org/search?q=Siriporn%20Chimphlee"> Siriporn Chimphlee</a>, <a href="https://publications.waset.org/search?q=Surat%20Srinoy"> Surat Srinoy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is important problems to increase the detection rates and reduce false positive rates in Intrusion Detection System (IDS). Although preventative techniques such as access control and authentication attempt to prevent intruders, these can fail, and as a second line of defence, intrusion detection has been introduced. Rare events are events that occur very infrequently, detection of rare events is a common problem in many domains. In this paper we propose an intrusion detection method that combines Rough set and Fuzzy Clustering. Rough set has to decrease the amount of data and get rid of redundancy. Fuzzy c-means clustering allow objects to belong to several clusters simultaneously, with different degrees of membership. Our approach allows us to recognize not only known attacks but also to detect suspicious activity that may be the result of a new, unknown attack. The experimental results on Knowledge Discovery and Data Mining-(KDDCup 1999) Dataset show that the method is efficient and practical for intrusion detection systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Network%20and%20security" title="Network and security">Network and security</a>, <a href="https://publications.waset.org/search?q=intrusion%20detection" title=" intrusion detection"> intrusion detection</a>, <a href="https://publications.waset.org/search?q=fuzzy%20cmeans" title=" fuzzy cmeans"> fuzzy cmeans</a>, <a href="https://publications.waset.org/search?q=rough%20set." title="rough set.">rough set.</a> </p> <a href="https://publications.waset.org/2474/unsupervised-clustering-methods-for-identifying-rare-events-in-anomaly-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/2474/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/2474/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/2474/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/2474/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/2474/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/2474/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/2474/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/2474/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/2474/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/2474/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/2474.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2861</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1536</span> Intrusion Detection Using a New Particle Swarm Method and Support Vector Machines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Essam%20Al%20Daoud">Essam Al Daoud</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Intrusion detection is a mechanism used to protect a system and analyse and predict the behaviours of system users. An ideal intrusion detection system is hard to achieve due to nonlinearity, and irrelevant or redundant features. This study introduces a new anomaly-based intrusion detection model. The suggested model is based on particle swarm optimisation and nonlinear, multi-class and multi-kernel support vector machines. Particle swarm optimisation is used for feature selection by applying a new formula to update the position and the velocity of a particle; the support vector machine is used as a classifier. The proposed model is tested and compared with the other methods using the KDD CUP 1999 dataset. The results indicate that this new method achieves better accuracy rates than previous methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Feature%20selection" title="Feature selection">Feature selection</a>, <a href="https://publications.waset.org/search?q=Intrusion%20detection" title=" Intrusion detection"> Intrusion detection</a>, <a href="https://publications.waset.org/search?q=Support%20vector%20machine" title=" Support vector machine"> Support vector machine</a>, <a href="https://publications.waset.org/search?q=Particle%20swarm." title=" Particle swarm."> Particle swarm.</a> </p> <a href="https://publications.waset.org/13220/intrusion-detection-using-a-new-particle-swarm-method-and-support-vector-machines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/13220/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/13220/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/13220/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/13220/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/13220/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/13220/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/13220/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/13220/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/13220/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/13220/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/13220.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1990</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1535</span> Development of Intelligent Time/Frequency Based Signal Detection Algorithm for Intrusion Detection System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Waqas%20Ahmed">Waqas Ahmed</a>, <a href="https://publications.waset.org/search?q=S%20Sajjad%20Haider%20Zaidi"> S Sajjad Haider Zaidi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For the past couple of decades Weak signal detection is of crucial importance in various engineering and scientific applications. It finds its application in areas like Wireless communication, Radars, Aerospace engineering, Control systems and many of those. Usually weak signal detection requires phase sensitive detector and demodulation module to detect and analyze the signal. This article gives you a preamble to intrusion detection system which can effectively detect a weak signal from a multiplexed signal. By carefully inspecting and analyzing the respective signal, this system can successfully indicate any peripheral intrusion. Intrusion detection system (IDS) is a comprehensive and easy approach towards detecting and analyzing any signal that is weakened and garbled due to low signal to noise ratio (SNR). This approach finds significant importance in applications like peripheral security systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Data%20Acquisition" title="Data Acquisition">Data Acquisition</a>, <a href="https://publications.waset.org/search?q=fast%20frequency%20transforms" title=" fast frequency transforms"> fast frequency transforms</a>, <a href="https://publications.waset.org/search?q=Lab%20VIEW%20software" title=" Lab VIEW software"> Lab VIEW software</a>, <a href="https://publications.waset.org/search?q=weak%20signal%20detection." title=" weak signal detection."> weak signal detection.</a> </p> <a href="https://publications.waset.org/8151/development-of-intelligent-timefrequency-based-signal-detection-algorithm-for-intrusion-detection-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/8151/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/8151/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/8151/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/8151/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/8151/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/8151/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/8151/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/8151/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/8151/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/8151/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/8151.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2510</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1534</span> Combine a Population-based Incremental Learning with Artificial Immune System for Intrusion Detection System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Jheng-Long%20Wu">Jheng-Long Wu</a>, <a href="https://publications.waset.org/search?q=Pei-Chann%20Chang"> Pei-Chann Chang</a>, <a href="https://publications.waset.org/search?q=Hsuan-Ming%20Chen"> Hsuan-Ming Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>This research focus on the intrusion detection system (IDS) development which using artificial immune system (AIS) with population based incremental learning (PBIL). AIS have powerful distinguished capability to extirpate antigen when the antigen intrude into human body. The PBIL is based on past learning experience to adjust new learning. Therefore we propose an intrusion detection system call PBIL-AIS which combine two approaches of PBIL and AIS to evolution computing. In AIS part we design three mechanisms such as clonal selection, negative selection and antibody level to intensify AIS performance. In experimental result, our PBIL-AIS IDS can capture high accuracy when an intrusion connection attacks.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Artificial%20immune%20system" title="Artificial immune system">Artificial immune system</a>, <a href="https://publications.waset.org/search?q=intrusion%20detection" title=" intrusion detection"> intrusion detection</a>, <a href="https://publications.waset.org/search?q=population-based%20incremental%20learning" title=" population-based incremental learning"> population-based incremental learning</a>, <a href="https://publications.waset.org/search?q=evolution%20computing." title=" evolution computing."> evolution computing.</a> </p> <a href="https://publications.waset.org/9454/combine-a-population-based-incremental-learning-with-artificial-immune-system-for-intrusion-detection-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9454/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9454/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9454/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9454/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9454/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9454/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9454/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9454/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9454/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9454/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9454.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1929</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1533</span> Svision: Visual Identification of Scanning and Denial of Service Attacks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Iosif-Viorel%20Onut">Iosif-Viorel Onut</a>, <a href="https://publications.waset.org/search?q=Bin%20Zhu"> Bin Zhu</a>, <a href="https://publications.waset.org/search?q=Ali%20A.%20Ghorbani"> Ali A. Ghorbani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose a novel graphical technique (SVision) for intrusion detection, which pictures the network as a community of hosts independently roaming in a 3D space defined by the set of services that they use. The aim of SVision is to graphically cluster the hosts into normal and abnormal ones, highlighting only the ones that are considered as a threat to the network. Our experimental results using DARPA 1999 and 2000 intrusion detection and evaluation datasets show the proposed technique as a good candidate for the detection of various threats of the network such as vertical and horizontal scanning, Denial of Service (DoS), and Distributed DoS (DDoS) attacks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Anomaly%20Visualization" title="Anomaly Visualization">Anomaly Visualization</a>, <a href="https://publications.waset.org/search?q=Network%20Security" title=" Network Security"> Network Security</a>, <a href="https://publications.waset.org/search?q=Intrusion%20Detection." title=" Intrusion Detection."> Intrusion Detection.</a> </p> <a href="https://publications.waset.org/7789/svision-visual-identification-of-scanning-and-denial-of-service-attacks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/7789/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/7789/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/7789/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/7789/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/7789/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/7789/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/7789/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/7789/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/7789/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/7789/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/7789.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1711</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1532</span> Intelligent Agents for Distributed Intrusion Detection System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=M.%20Benattou">M. Benattou</a>, <a href="https://publications.waset.org/search?q=K.%20Tamine"> K. Tamine</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a distributed intrusion detection system IDS, based on the concept of specialized distributed agents community representing agents with the same purpose for detecting distributed attacks. The semantic of intrusion events occurring in a predetermined network has been defined. The correlation rules referring the process which our proposed IDS combines the captured events that is distributed both spatially and temporally. And then the proposed IDS tries to extract significant and broad patterns for set of well-known attacks. The primary goal of our work is to provide intrusion detection and real-time prevention capability against insider attacks in distributed and fully automated environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Mobile%20agent" title="Mobile agent">Mobile agent</a>, <a href="https://publications.waset.org/search?q=specialized%20agent" title=" specialized agent"> specialized agent</a>, <a href="https://publications.waset.org/search?q=interpreter%20agent" title=" interpreter agent"> interpreter agent</a>, <a href="https://publications.waset.org/search?q=event%20rules" title=" event rules"> event rules</a>, <a href="https://publications.waset.org/search?q=correlation." title=" correlation."> correlation.</a> </p> <a href="https://publications.waset.org/14763/intelligent-agents-for-distributed-intrusion-detection-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/14763/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/14763/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/14763/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/14763/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/14763/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/14763/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/14763/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/14763/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/14763/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/14763/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/14763.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1834</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1531</span> Incorporating Multiple Supervised Learning Algorithms for Effective Intrusion Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Umar%20Albalawi">Umar Albalawi</a>, <a href="https://publications.waset.org/search?q=Sang%20C.%20Suh"> Sang C. Suh</a>, <a href="https://publications.waset.org/search?q=Jinoh%20Kim"> Jinoh Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>As internet continues to expand its usage with an  enormous number of applications, cyber-threats have significantly  increased accordingly. Thus, accurate detection of malicious traffic in  a timely manner is a critical concern in today’s Internet for security.  One approach for intrusion detection is to use Machine Learning (ML)  techniques. Several methods based on ML algorithms have been  introduced over the past years, but they are largely limited in terms of  detection accuracy and/or time and space complexity to run. In this  work, we present a novel method for intrusion detection that  incorporates a set of supervised learning algorithms. The proposed  technique provides high accuracy and outperforms existing techniques  that simply utilizes a single learning method. In addition, our  technique relies on partial flow information (rather than full  information) for detection, and thus, it is light-weight and desirable for  online operations with the property of early identification. With the  mid-Atlantic CCDC intrusion dataset publicly available, we show that  our proposed technique yields a high degree of detection rate over 99%  with a very low false alarm rate (0.4%). </p> <p> </p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Intrusion%20Detection" title="Intrusion Detection">Intrusion Detection</a>, <a href="https://publications.waset.org/search?q=Supervised%20Learning" title=" Supervised Learning"> Supervised Learning</a>, <a href="https://publications.waset.org/search?q=Traffic%20%0D%0AClassification." title=" Traffic Classification."> Traffic Classification.</a> </p> <a href="https://publications.waset.org/9997533/incorporating-multiple-supervised-learning-algorithms-for-effective-intrusion-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9997533/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9997533/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9997533/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9997533/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9997533/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9997533/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9997533/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9997533/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9997533/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9997533/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9997533.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2035</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1530</span> Network Anomaly Detection using Soft Computing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Surat%20Srinoy">Surat Srinoy</a>, <a href="https://publications.waset.org/search?q=Werasak%20Kurutach"> Werasak Kurutach</a>, <a href="https://publications.waset.org/search?q=Witcha%20Chimphlee"> Witcha Chimphlee</a>, <a href="https://publications.waset.org/search?q=Siriporn%20Chimphlee"> Siriporn Chimphlee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One main drawback of intrusion detection system is the inability of detecting new attacks which do not have known signatures. In this paper we discuss an intrusion detection method that proposes independent component analysis (ICA) based feature selection heuristics and using rough fuzzy for clustering data. ICA is to separate these independent components (ICs) from the monitored variables. Rough set has to decrease the amount of data and get rid of redundancy and Fuzzy methods allow objects to belong to several clusters simultaneously, with different degrees of membership. Our approach allows us to recognize not only known attacks but also to detect activity that may be the result of a new, unknown attack. The experimental results on Knowledge Discovery and Data Mining- (KDDCup 1999) dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Network%20security" title="Network security">Network security</a>, <a href="https://publications.waset.org/search?q=intrusion%20detection" title=" intrusion detection"> intrusion detection</a>, <a href="https://publications.waset.org/search?q=rough%20set" title=" rough set"> rough set</a>, <a href="https://publications.waset.org/search?q=ICA" title=" ICA"> ICA</a>, <a href="https://publications.waset.org/search?q=anomaly%20detection" title=" anomaly detection"> anomaly detection</a>, <a href="https://publications.waset.org/search?q=independent%20component%20analysis" title=" independent component analysis"> independent component analysis</a>, <a href="https://publications.waset.org/search?q=rough%0Afuzzy%20." title=" rough fuzzy ."> rough fuzzy .</a> </p> <a href="https://publications.waset.org/14879/network-anomaly-detection-using-soft-computing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/14879/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/14879/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/14879/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/14879/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/14879/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/14879/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/14879/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/14879/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/14879/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/14879/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/14879.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1955</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1529</span> Apoptosis Inspired Intrusion Detection System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=R.%20Sridevi">R. Sridevi</a>, <a href="https://publications.waset.org/search?q=G.%20Jagajothi"> G. Jagajothi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Artificial Immune Systems (AIS), inspired by the human immune system, are algorithms and mechanisms which are self-adaptive and self-learning classifiers capable of recognizing and classifying by learning, long-term memory and association. Unlike other human system inspired techniques like genetic algorithms and neural networks, AIS includes a range of algorithms modeling on different immune mechanism of the body. In this paper, a mechanism of a human immune system based on apoptosis is adopted to build an Intrusion Detection System (IDS) to protect computer networks. Features are selected from network traffic using Fisher Score. Based on the selected features, the record/connection is classified as either an attack or normal traffic by the proposed methodology. Simulation results demonstrates that the proposed AIS based on apoptosis performs better than existing AIS for intrusion detection.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Apoptosis" title="Apoptosis">Apoptosis</a>, <a href="https://publications.waset.org/search?q=Artificial%20Immune%20System%20%28AIS%29" title=" Artificial Immune System (AIS)"> Artificial Immune System (AIS)</a>, <a href="https://publications.waset.org/search?q=Fisher%0D%0AScore" title=" Fisher Score"> Fisher Score</a>, <a href="https://publications.waset.org/search?q=KDD%20dataset" title=" KDD dataset"> KDD dataset</a>, <a href="https://publications.waset.org/search?q=Network%20intrusion%20detection." title=" Network intrusion detection."> Network intrusion detection.</a> </p> <a href="https://publications.waset.org/10000033/apoptosis-inspired-intrusion-detection-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10000033/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10000033/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10000033/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10000033/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10000033/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10000033/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10000033/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10000033/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10000033/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10000033/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10000033.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2191</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1528</span> Feature Based Unsupervised Intrusion Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Deeman%20Yousif%20Mahmood">Deeman Yousif Mahmood</a>, <a href="https://publications.waset.org/search?q=Mohammed%20Abdullah%20Hussein"> Mohammed Abdullah Hussein</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>The goal of a network-based intrusion detection system is to classify activities of network traffics into two major categories: normal and attack (intrusive) activities. Nowadays, data mining and machine learning plays an important role in many sciences; including intrusion detection system (IDS) using both supervised and unsupervised techniques. However, one of the essential steps of data mining is feature selection that helps in improving the efficiency, performance and prediction rate of proposed approach. This paper applies unsupervised K-means clustering algorithm with information gain (IG) for feature selection and reduction to build a network intrusion detection system. For our experimental analysis, we have used the new NSL-KDD dataset, which is a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. With a split of 60.0% for the training set and the remainder for the testing set, a 2 class classifications have been implemented (Normal, Attack). Weka framework which is a java based open source software consists of a collection of machine learning algorithms for data mining tasks has been used in the testing process. The experimental results show that the proposed approach is very accurate with low false positive rate and high true positive rate and it takes less learning time in comparison with using the full features of the dataset with the same algorithm.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Information%20Gain%20%28IG%29" title="Information Gain (IG)">Information Gain (IG)</a>, <a href="https://publications.waset.org/search?q=Intrusion%20Detection%20System%0D%0A%28IDS%29" title=" Intrusion Detection System (IDS)"> Intrusion Detection System (IDS)</a>, <a href="https://publications.waset.org/search?q=K-means%20Clustering" title=" K-means Clustering"> K-means Clustering</a>, <a href="https://publications.waset.org/search?q=Weka." title=" Weka."> Weka.</a> </p> <a href="https://publications.waset.org/9999865/feature-based-unsupervised-intrusion-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9999865/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9999865/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9999865/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9999865/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9999865/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9999865/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9999865/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9999865/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9999865/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9999865/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9999865.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2776</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1527</span> Off-Policy Q-learning Technique for Intrusion Response in Network Security</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Zheni%20S.%20Stefanova">Zheni S. Stefanova</a>, <a href="https://publications.waset.org/search?q=Kandethody%20M.%20Ramachandran"> Kandethody M. Ramachandran</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the increasing dependency on our computer devices, we face the necessity of adequate, efficient and effective mechanisms, for protecting our network. There are two main problems that Intrusion Detection Systems (IDS) attempt to solve. 1) To detect the attack, by analyzing the incoming traffic and inspect the network (intrusion detection). 2) To produce a prompt response when the attack occurs (intrusion prevention). It is critical creating an Intrusion detection model that will detect a breach in the system on time and also challenging making it provide an automatic and with an acceptable delay response at every single stage of the monitoring process. We cannot afford to adopt security measures with a high exploiting computational power, and we are not able to accept a mechanism that will react with a delay. In this paper, we will propose an intrusion response mechanism that is based on artificial intelligence, and more precisely, reinforcement learning techniques (RLT). The RLT will help us to create a decision agent, who will control the process of interacting with the undetermined environment. The goal is to find an optimal policy, which will represent the intrusion response, therefore, to solve the Reinforcement learning problem, using a Q-learning approach. Our agent will produce an optimal immediate response, in the process of evaluating the network traffic.This Q-learning approach will establish the balance between exploration and exploitation and provide a unique, self-learning and strategic artificial intelligence response mechanism for IDS. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Intrusion%20prevention" title="Intrusion prevention">Intrusion prevention</a>, <a href="https://publications.waset.org/search?q=network%20security" title=" network security"> network security</a>, <a href="https://publications.waset.org/search?q=optimal%20policy" title=" optimal policy"> optimal policy</a>, <a href="https://publications.waset.org/search?q=Q-learning." title=" Q-learning."> Q-learning.</a> </p> <a href="https://publications.waset.org/10008916/off-policy-q-learning-technique-for-intrusion-response-in-network-security" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10008916/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10008916/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10008916/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10008916/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10008916/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10008916/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10008916/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10008916/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10008916/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10008916/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10008916.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1022</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1526</span> A Software of Intrusion Detection Mechanism for Virtual Platforms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Ying-Chuan%20Chen">Ying-Chuan Chen</a>, <a href="https://publications.waset.org/search?q=Shuen-Tai%20Wang"> Shuen-Tai Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Security is an interesting and significance issue for popular virtual platforms, such as virtualization cluster and cloud platforms. Virtualization is the powerful technology for cloud computing services, there are a lot of benefits by using virtual machine tools which be called hypervisors, such as it can quickly deploy all kinds of virtual Operating Systems in single platform, able to control all virtual system resources effectively, cost down for system platform deployment, ability of customization, high elasticity and high reliability. However, some important security problems need to take care and resolved in virtual platforms that include terrible viruses, evil programs, illegal operations and intrusion behavior. In this paper, we present useful Intrusion Detection Mechanism (IDM) software that not only can auto to analyze all system-s operations with the accounting journal database, but also is able to monitor the system-s state for virtual platforms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=security" title="security">security</a>, <a href="https://publications.waset.org/search?q=cluster" title=" cluster"> cluster</a>, <a href="https://publications.waset.org/search?q=cloud" title=" cloud"> cloud</a>, <a href="https://publications.waset.org/search?q=virtualization" title=" virtualization"> virtualization</a>, <a href="https://publications.waset.org/search?q=virtual%0Amachine" title=" virtual machine"> virtual machine</a>, <a href="https://publications.waset.org/search?q=virus" title=" virus"> virus</a>, <a href="https://publications.waset.org/search?q=intrusion%20detection" title=" intrusion detection"> intrusion detection</a> </p> <a href="https://publications.waset.org/3263/a-software-of-intrusion-detection-mechanism-for-virtual-platforms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/3263/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/3263/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/3263/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/3263/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/3263/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/3263/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/3263/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/3263/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/3263/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/3263/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/3263.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1546</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1525</span> A Model of Network Security with Prevention Capability by Using Decoy Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Supachai%20Tangwongsan">Supachai Tangwongsan</a>, <a href="https://publications.waset.org/search?q=Labhidhorn%20Pangphuthipong"> Labhidhorn Pangphuthipong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>This research work proposes a model of network security systems aiming to prevent production system in a data center from being attacked by intrusions. Conceptually, we introduce a decoy system as a part of the security system for luring intrusions, and apply network intrusion detection (NIDS), coupled with the decoy system to perform intrusion prevention. When NIDS detects an activity of intrusions, it will signal a redirection module to redirect all malicious traffics to attack the decoy system instead, and hence the production system is protected and safe. However, in a normal situation, traffic will be simply forwarded to the production system as usual. Furthermore, we assess the performance of the model with various bandwidths, packet sizes and inter-attack intervals (attacking frequencies).</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Intrusion%20detection" title="Intrusion detection">Intrusion detection</a>, <a href="https://publications.waset.org/search?q=Decoy" title=" Decoy"> Decoy</a>, <a href="https://publications.waset.org/search?q=Snort" title=" Snort"> Snort</a>, <a href="https://publications.waset.org/search?q=Intrusion%20prevention." title=" Intrusion prevention."> Intrusion prevention.</a> </p> <a href="https://publications.waset.org/10459/a-model-of-network-security-with-prevention-capability-by-using-decoy-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10459/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10459/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10459/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10459/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10459/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10459/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10459/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10459/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10459/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10459/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10459.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1748</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1524</span> A Survey in Techniques for Imbalanced Intrusion Detection System Datasets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Najmeh%20Abedzadeh">Najmeh Abedzadeh</a>, <a href="https://publications.waset.org/search?q=Matthew%20Jacobs"> Matthew Jacobs</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>An intrusion detection system (IDS) is a software application that monitors malicious activities and generates alerts if any are detected. However, most network activities in IDS datasets are normal, and the relatively few numbers of attacks make the available data imbalanced. Consequently, cyber-attacks can hide inside a large number of normal activities, and machine learning algorithms have difficulty learning and classifying the data correctly. In this paper, a comprehensive literature review is conducted on different types of algorithms for both implementing the IDS and methods in correcting the imbalanced IDS dataset. The most famous algorithms are machine learning (ML), deep learning (DL), synthetic minority over-sampling technique (SMOTE), and reinforcement learning (RL). Most of the research use the CSE-CIC-IDS2017, CSE-CIC-IDS2018, and NSL-KDD datasets for evaluating their algorithms.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=IDS" title="IDS">IDS</a>, <a href="https://publications.waset.org/search?q=intrusion%20detection%20system" title=" intrusion detection system"> intrusion detection system</a>, <a href="https://publications.waset.org/search?q=imbalanced%20datasets" title=" imbalanced datasets"> imbalanced datasets</a>, <a href="https://publications.waset.org/search?q=sampling%20algorithms" title=" sampling algorithms"> sampling algorithms</a>, <a href="https://publications.waset.org/search?q=big%20data." title=" big data."> big data.</a> </p> <a href="https://publications.waset.org/10012884/a-survey-in-techniques-for-imbalanced-intrusion-detection-system-datasets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10012884/apa" target="_blank" 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