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detection system</title> <meta name="description" content="Search results for: intrusion detection system"> <meta name="keywords" content="intrusion detection system"> <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 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action="https://publications.waset.org/abstracts/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 system"> <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> 20053</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: intrusion detection system</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20053</span> An Architecture for New Generation of Distributed Intrusion Detection System Based on Preventive Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Benmoussa">H. Benmoussa</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20A.%20El%20Kalam"> A. A. El Kalam</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Ait%20Ouahman"> A. Ait Ouahman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The design and implementation of intrusion detection systems (IDS) remain an important area of research in the security of information systems. Despite the importance and reputation of the current intrusion detection systems, their efficiency and effectiveness remain limited as they should include active defense approach to allow anticipating and predicting intrusions before their occurrence. Consequently, they must be readapted. For this purpose we suggest a new generation of distributed intrusion detection system based on preventive detection approach and using intelligent and mobile agents. Our architecture benefits from mobile agent features and addresses some of the issues with centralized and hierarchical models. Also, it presents advantages in terms of increasing scalability and flexibility. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Intrusion%20Detection%20System%20%28IDS%29" title="Intrusion Detection System (IDS)">Intrusion Detection System (IDS)</a>, <a href="https://publications.waset.org/abstracts/search?q=preventive%20detection" title=" preventive detection"> preventive detection</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20agents" title=" mobile agents"> mobile agents</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20architecture" title=" distributed architecture"> distributed architecture</a> </p> <a href="https://publications.waset.org/abstracts/18239/an-architecture-for-new-generation-of-distributed-intrusion-detection-system-based-on-preventive-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18239.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">583</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20052</span> Securing Web Servers by the Intrusion Detection System (IDS)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yousef%20Farhaoui">Yousef Farhaoui </a> </p> <p class="card-text"><strong>Abstract:</strong></p> An IDS is a tool which is used to improve the level of security. We present in this paper different architectures of IDS. We will also discuss measures that define the effectiveness of IDS and the very recent works of standardization and homogenization of IDS. At the end, we propose a new model of IDS called BiIDS (IDS Based on the two principles of detection) for securing web servers and applications by the Intrusion Detection System (IDS). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection" title="intrusion detection">intrusion detection</a>, <a href="https://publications.waset.org/abstracts/search?q=architectures" title=" architectures"> architectures</a>, <a href="https://publications.waset.org/abstracts/search?q=characteristic" title=" characteristic"> characteristic</a>, <a href="https://publications.waset.org/abstracts/search?q=tools" title=" tools"> tools</a>, <a href="https://publications.waset.org/abstracts/search?q=security" title=" security"> security</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20server" title=" web server"> web server</a> </p> <a href="https://publications.waset.org/abstracts/13346/securing-web-servers-by-the-intrusion-detection-system-ids" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13346.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">418</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20051</span> Proposed Anticipating Learning Classifier System for Cloud Intrusion Detection (ALCS-CID)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wafa%27%20Slaibi%20Alsharafat">Wafa' Slaibi Alsharafat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cloud computing is a modern approach in network environment. According to increased number of network users and online systems, there is a need to help these systems to be away from unauthorized resource access and detect any attempts for privacy contravention. For that purpose, Intrusion Detection System is an effective security mechanism to detect any attempts of attacks for cloud resources and their information. In this paper, Cloud Intrusion Detection System has been proposed in term of reducing or eliminating any attacks. This model concerns about achieving high detection rate after conducting a set of experiments using benchmarks dataset called KDD'99. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=IDS" title="IDS">IDS</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title=" cloud computing"> cloud computing</a>, <a href="https://publications.waset.org/abstracts/search?q=anticipating%20classifier%20system" title=" anticipating classifier system"> anticipating classifier system</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection" title=" intrusion detection"> intrusion detection</a> </p> <a href="https://publications.waset.org/abstracts/18240/proposed-anticipating-learning-classifier-system-for-cloud-intrusion-detection-alcs-cid" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18240.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">474</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20050</span> Combination between Intrusion Systems and Honeypots</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Majed%20Sanan">Majed Sanan</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Rammal"> Mohammad Rammal</a>, <a href="https://publications.waset.org/abstracts/search?q=Wassim%20Rammal"> Wassim Rammal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Today, security is a major concern. Intrusion Detection, Prevention Systems and Honeypot can be used to moderate attacks. Many researchers have proposed to use many IDSs ((Intrusion Detection System) time to time. Some of these IDS’s combine their features of two or more IDSs which are called Hybrid Intrusion Detection Systems. Most of the researchers combine the features of Signature based detection methodology and Anomaly based detection methodology. For a signature based IDS, if an attacker attacks slowly and in organized way, the attack may go undetected through the IDS, as signatures include factors based on duration of the events but the actions of attacker do not match. Sometimes, for an unknown attack there is no signature updated or an attacker attack in the mean time when the database is updating. Thus, signature-based IDS fail to detect unknown attacks. Anomaly based IDS suffer from many false-positive readings. So there is a need to hybridize those IDS which can overcome the shortcomings of each other. In this paper we propose a new approach to IDS (Intrusion Detection System) which is more efficient than the traditional IDS (Intrusion Detection System). The IDS is based on Honeypot Technology and Anomaly based Detection Methodology. We have designed Architecture for the IDS in a packet tracer and then implemented it in real time. We have discussed experimental results performed: both the Honeypot and Anomaly based IDS have some shortcomings but if we hybridized these two technologies, the newly proposed Hybrid Intrusion Detection System (HIDS) is capable enough to overcome these shortcomings with much enhanced performance. In this paper, we present a modified Hybrid Intrusion Detection System (HIDS) that combines the positive features of two different detection methodologies - Honeypot methodology and anomaly based intrusion detection methodology. In the experiment, we ran both the Intrusion Detection System individually first and then together and recorded the data from time to time. From the data we can conclude that the resulting IDS are much better in detecting intrusions from the existing IDSs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=security" title="security">security</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection" title=" intrusion detection"> intrusion detection</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20prevention" title=" intrusion prevention"> intrusion prevention</a>, <a href="https://publications.waset.org/abstracts/search?q=honeypot" title=" honeypot"> honeypot</a>, <a href="https://publications.waset.org/abstracts/search?q=anomaly-based%20detection" title=" anomaly-based detection"> anomaly-based detection</a>, <a href="https://publications.waset.org/abstracts/search?q=signature-based%20detection" title=" signature-based detection"> signature-based detection</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title=" cloud computing"> cloud computing</a>, <a href="https://publications.waset.org/abstracts/search?q=kfsensor" title=" kfsensor"> kfsensor</a> </p> <a href="https://publications.waset.org/abstracts/40174/combination-between-intrusion-systems-and-honeypots" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40174.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">382</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20049</span> Intrusion Detection System Based on Peer to Peer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alireza%20Pour%20Ebrahimi">Alireza Pour Ebrahimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Vahid%20Abasi"> Vahid Abasi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently by the extension of internet usage, Research on the intrusion detection system takes a significant importance. Many of improvement systems prevent internal and external network attacks by providing security through firewalls and antivirus. In recently years, intrusion detection systems gradually turn from host-based systems and depend on O.S to the distributed systems which are running on multiple O.S. In this work, by considering the diversity of computer networks whit respect to structure, architecture, resource, services, users and also security goals requirement a fully distributed collaborative intrusion detection system based on peer to peer architecture is suggested. in this platform each partner device (matched device) considered as a peer-to-peer network. All transmitted information to network are visible only for device that use security scanning of a source. Experimental results show that the distributed architecture is significantly upgradeable in respect to centralized approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=network" title="network">network</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection%20system" title=" intrusion detection system"> intrusion detection system</a>, <a href="https://publications.waset.org/abstracts/search?q=peer%20to%20peer" title=" peer to peer"> peer to peer</a>, <a href="https://publications.waset.org/abstracts/search?q=internal%20and%20external%20network" title=" internal and external network "> internal and external network </a> </p> <a href="https://publications.waset.org/abstracts/25216/intrusion-detection-system-based-on-peer-to-peer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25216.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">547</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20048</span> Intrusion Detection and Prevention System (IDPS) in Cloud Computing Using Anomaly-Based and Signature-Based Detection Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=John%20Onyima">John Onyima</a>, <a href="https://publications.waset.org/abstracts/search?q=Ikechukwu%20Ezepue"> Ikechukwu Ezepue</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Virtualization and cloud computing are among the fast-growing computing innovations in recent times. Organisations all over the world are moving their computing services towards the cloud this is because of its rapid transformation of the organization’s infrastructure and improvement of efficient resource utilization and cost reduction. However, this technology brings new security threats and challenges about safety, reliability and data confidentiality. Evidently, no single security technique can guarantee security or protection against malicious attacks on a cloud computing network hence an integrated model of intrusion detection and prevention system has been proposed. Anomaly-based and signature-based detection techniques will be integrated to enable the network and its host defend themselves with some level of intelligence. The anomaly-base detection was implemented using the local deviation factor graph-based (LDFGB) algorithm while the signature-based detection was implemented using the snort algorithm. Results from this collaborative intrusion detection and prevention techniques show robust and efficient security architecture for cloud computing networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anomaly-based%20detection" title="anomaly-based detection">anomaly-based detection</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title=" cloud computing"> cloud computing</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection" title=" intrusion detection"> intrusion detection</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20prevention" title=" intrusion prevention"> intrusion prevention</a>, <a href="https://publications.waset.org/abstracts/search?q=signature-based%20detection" title=" signature-based detection"> signature-based detection</a> </p> <a href="https://publications.waset.org/abstracts/89892/intrusion-detection-and-prevention-system-idps-in-cloud-computing-using-anomaly-based-and-signature-based-detection-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89892.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">305</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20047</span> Design of an Improved Distributed Framework for Intrusion Detection System Based on Artificial Immune System and Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yulin%20Rao">Yulin Rao</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhixuan%20Li"> Zhixuan Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Burra%20Venkata%20Durga%20Kumar"> Burra Venkata Durga Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Intrusion detection refers to monitoring the actions of internal and external intruders on the system and detecting the behaviours that violate security policies in real-time. In intrusion detection, there has been much discussion about the application of neural network technology and artificial immune system (AIS). However, many solutions use static methods (signature-based and stateful protocol analysis) or centralized intrusion detection systems (CIDS), which are unsuitable for real-time intrusion detection systems that need to process large amounts of data and detect unknown intrusions. This article proposes a framework for a distributed intrusion detection system (DIDS) with multi-agents based on the concept of AIS and neural network technology to detect anomalies and intrusions. In this framework, multiple agents are assigned to each host and work together, improving the system's detection efficiency and robustness. The trainer agent in the central server of the framework uses the artificial neural network (ANN) rather than the negative selection algorithm of AIS to generate mature detectors. Mature detectors can distinguish between self-files and non-self-files after learning. Our analyzer agents use genetic algorithms to generate memory cell detectors. This kind of detector will effectively reduce false positive and false negative errors and act quickly on known intrusions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20immune%20system" title="artificial immune system">artificial immune system</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20artificial%20intelligence" title=" distributed artificial intelligence"> distributed artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-agent" title=" multi-agent"> multi-agent</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection%20system" title=" intrusion detection system"> intrusion detection system</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a> </p> <a href="https://publications.waset.org/abstracts/152818/design-of-an-improved-distributed-framework-for-intrusion-detection-system-based-on-artificial-immune-system-and-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152818.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">109</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20046</span> A Survey on Genetic Algorithm for Intrusion Detection System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prikhil%20Agrawal">Prikhil Agrawal</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Priyanka"> N. Priyanka</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the increase of millions of users on Internet day by day, it is very essential to maintain highly reliable and secured data communication between various corporations. Although there are various traditional security imparting techniques such as antivirus software, password protection, data encryption, biometrics and firewall etc. But still network security has become the main issue in various leading companies. So IDSs have become an essential component in terms of security, as it can detect various network attacks and respond quickly to such occurrences. IDSs are used to detect unauthorized access to a computer system. This paper describes various intrusion detection techniques using GA approach. The intrusion detection problem has become a challenging task due to the conception of miscellaneous computer networks under various vulnerabilities. Thus the damage caused to various organizations by malicious intrusions can be mitigated and even be deterred by using this powerful tool. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm%20%28GA%29" title="genetic algorithm (GA)">genetic algorithm (GA)</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection%20system%20%28IDS%29" title=" intrusion detection system (IDS)"> intrusion detection system (IDS)</a>, <a href="https://publications.waset.org/abstracts/search?q=dataset" title=" dataset"> dataset</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20security" title=" network security"> network security</a> </p> <a href="https://publications.waset.org/abstracts/71298/a-survey-on-genetic-algorithm-for-intrusion-detection-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71298.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">297</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20045</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/abstracts/search?q=L.%20Sellami">L. Sellami</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Idoughi"> D. Idoughi</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20F.%20Tiako"> P. F. Tiako</a> </p> <p class="card-text"><strong>Abstract:</strong></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 class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title="cloud computing">cloud computing</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection%20system" title=" intrusion detection system"> intrusion detection system</a>, <a href="https://publications.waset.org/abstracts/search?q=privacy" title=" privacy"> privacy</a>, <a href="https://publications.waset.org/abstracts/search?q=trust" title=" trust"> trust</a> </p> <a href="https://publications.waset.org/abstracts/63446/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/abstracts/63446.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">323</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20044</span> Intrusion Detection Techniques in NaaS in the Cloud: A Review </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rashid%20Mahmood">Rashid Mahmood</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The network as a service (NaaS) usage has been well-known from the last few years in the many applications, like mission critical applications. In the NaaS, prevention method is not adequate as the security concerned, so the detection method should be added to the security issues in NaaS. The authentication and encryption are considered the first solution of the NaaS problem whereas now these are not sufficient as NaaS use is increasing. In this paper, we are going to present the concept of intrusion detection and then survey some of major intrusion detection techniques in NaaS and aim to compare in some important fields. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=IDS" title="IDS">IDS</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud" title=" cloud"> cloud</a>, <a href="https://publications.waset.org/abstracts/search?q=naas" title=" naas"> naas</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a> </p> <a href="https://publications.waset.org/abstracts/36475/intrusion-detection-techniques-in-naas-in-the-cloud-a-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36475.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">320</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20043</span> e-Learning Security: A Distributed Incident Response Generator</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bel%20G%20Raggad">Bel G Raggad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An e-Learning setting is a distributed computing environment where information resources can be connected to any public network. Public networks are very unsecure which can compromise the reliability of an e-Learning environment. This study is only concerned with the intrusion detection aspect of e-Learning security and how incident responses are planned. The literature reported great advances in intrusion detection system (ids) but neglected to study an important ids weakness: suspected events are detected but an intrusion is not determined because it is not defined in ids databases. We propose an incident response generator (DIRG) that produces incident responses when the working ids system suspects an event that does not correspond to a known intrusion. Data involved in intrusion detection when ample uncertainty is present is often not suitable to formal statistical models including Bayesian. We instead adopt Dempster and Shafer theory to process intrusion data for the unknown event. The DIRG engine transforms data into a belief structure using incident scenarios deduced by the security administrator. Belief values associated with various incident scenarios are then derived and evaluated to choose the most appropriate scenario for which an automatic incident response is generated. This article provides a numerical example demonstrating the working of the DIRG system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20support%20system" title="decision support system">decision support system</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20computing" title=" distributed computing"> distributed computing</a>, <a href="https://publications.waset.org/abstracts/search?q=e-Learning%20security" title=" e-Learning security"> e-Learning security</a>, <a href="https://publications.waset.org/abstracts/search?q=incident%20response" title=" incident response"> incident response</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection" title=" intrusion detection"> intrusion detection</a>, <a href="https://publications.waset.org/abstracts/search?q=security%20risk" title=" security risk"> security risk</a>, <a href="https://publications.waset.org/abstracts/search?q=statefull%20inspection" title=" statefull inspection"> statefull inspection</a> </p> <a href="https://publications.waset.org/abstracts/23292/e-learning-security-a-distributed-incident-response-generator" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23292.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">437</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20042</span> Intrusion Detection Based on Graph Oriented Big Data Analytics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahlem%20Abid">Ahlem Abid</a>, <a href="https://publications.waset.org/abstracts/search?q=Farah%20%20Jemili"> Farah Jemili</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Intrusion detection has been the subject of numerous studies in industry and academia, but cyber security analysts always want greater precision and global threat analysis to secure their systems in cyberspace. To improve intrusion detection system, the visualisation of the security events in form of graphs and diagrams is important to improve the accuracy of alerts. In this paper, we propose an approach of an IDS based on cloud computing, big data technique and using a machine learning graph algorithm which can detect in real time different attacks as early as possible. We use the MAWILab intrusion detection dataset . We choose Microsoft Azure as a unified cloud environment to load our dataset on. We implement the k2 algorithm which is a graphical machine learning algorithm to classify attacks. Our system showed a good performance due to the graphical machine learning algorithm and spark structured streaming engine. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Apache%20Spark%20Streaming" title="Apache Spark Streaming">Apache Spark Streaming</a>, <a href="https://publications.waset.org/abstracts/search?q=Graph" title=" Graph"> Graph</a>, <a href="https://publications.waset.org/abstracts/search?q=Intrusion%20detection" title=" Intrusion detection"> Intrusion detection</a>, <a href="https://publications.waset.org/abstracts/search?q=k2%20algorithm" title=" k2 algorithm"> k2 algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Machine%20Learning" title=" Machine Learning"> Machine Learning</a>, <a href="https://publications.waset.org/abstracts/search?q=MAWILab" title=" MAWILab"> MAWILab</a>, <a href="https://publications.waset.org/abstracts/search?q=Microsoft%20Azure%20Cloud" title=" Microsoft Azure Cloud"> Microsoft Azure Cloud</a> </p> <a href="https://publications.waset.org/abstracts/127073/intrusion-detection-based-on-graph-oriented-big-data-analytics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127073.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">146</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20041</span> Intrusion Detection in Computer Networks Using a Hybrid Model of Firefly and Differential Evolution Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Besharatloo">Mohammad Besharatloo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Intrusion detection is an important research topic in network security because of increasing growth in the use of computer network services. Intrusion detection is done with the aim of detecting the unauthorized use or abuse in the networks and systems by the intruders. Therefore, the intrusion detection system is an efficient tool to control the user's access through some predefined regulations. Since, the data used in intrusion detection system has high dimension, a proper representation is required to show the basis structure of this data. Therefore, it is necessary to eliminate the redundant features to create the best representation subset. In the proposed method, a hybrid model of differential evolution and firefly algorithms was employed to choose the best subset of properties. In addition, decision tree and support vector machine (SVM) are adopted to determine the quality of the selected properties. In the first, the sorted population is divided into two sub-populations. These optimization algorithms were implemented on these sub-populations, respectively. Then, these sub-populations are merged to create next repetition population. The performance evaluation of the proposed method is done based on KDD Cup99. The simulation results show that the proposed method has better performance than the other methods in this context. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection%20system" title="intrusion detection system">intrusion detection system</a>, <a href="https://publications.waset.org/abstracts/search?q=differential%20evolution" title=" differential evolution"> differential evolution</a>, <a href="https://publications.waset.org/abstracts/search?q=firefly%20algorithm" title=" firefly algorithm"> firefly algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a> </p> <a href="https://publications.waset.org/abstracts/165079/intrusion-detection-in-computer-networks-using-a-hybrid-model-of-firefly-and-differential-evolution-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165079.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">91</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20040</span> Intrusion Detection Techniques in Mobile Adhoc Networks: A Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rashid%20Mahmood">Rashid Mahmood</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Junaid%20Sarwar"> Muhammad Junaid Sarwar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mobile ad hoc networks (MANETs) use has been well-known from the last few years in the many applications, like mission critical applications. In the (MANETS) prevention method is not adequate as the security concerned, so the detection method should be added to the security issues in (MANETs). The authentication and encryption is considered the first solution of the MANETs problem where as now these are not sufficient as MANET use is increasing. In this paper we are going to present the concept of intrusion detection and then survey some of major intrusion detection techniques in MANET and aim to comparing in some important fields. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MANET" title="MANET">MANET</a>, <a href="https://publications.waset.org/abstracts/search?q=IDS" title=" IDS"> IDS</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusions" title=" intrusions"> intrusions</a>, <a href="https://publications.waset.org/abstracts/search?q=signature" title=" signature"> signature</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a>, <a href="https://publications.waset.org/abstracts/search?q=prevention" title=" prevention"> prevention</a> </p> <a href="https://publications.waset.org/abstracts/32173/intrusion-detection-techniques-in-mobile-adhoc-networks-a-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32173.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">378</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20039</span> Feature Based Unsupervised Intrusion Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Deeman%20Yousif%20Mahmood">Deeman Yousif Mahmood</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Abdullah%20Hussein"> Mohammed Abdullah Hussein</a> </p> <p class="card-text"><strong>Abstract:</strong></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 class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=information%20gain%20%28IG%29" title="information gain (IG)">information gain (IG)</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection%20system%20%28IDS%29" title=" intrusion detection system (IDS)"> intrusion detection system (IDS)</a>, <a href="https://publications.waset.org/abstracts/search?q=k-means%20clustering" title=" k-means clustering"> k-means clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=Weka" title=" Weka"> Weka</a> </p> <a href="https://publications.waset.org/abstracts/5974/feature-based-unsupervised-intrusion-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5974.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">296</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20038</span> Intrusion Detection Using Dual Artificial Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rana%20I.%20Abdulghani">Rana I. Abdulghani</a>, <a href="https://publications.waset.org/abstracts/search?q=Amera%20I.%20Melhum"> Amera I. Melhum</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the abnormal growth of the usage of computers over networks and under the consideration or agreement of most of the computer security experts who said that the goal of building a secure system is never achieved effectively, all these points led to the design of the intrusion detection systems(IDS). This research adopts a comparison between two techniques for network intrusion detection, The first one used the (Particles Swarm Optimization) that fall within the field (Swarm Intelligence). In this Act, the algorithm Enhanced for the purpose of obtaining the minimum error rate by amending the cluster centers when better fitness function is found through the training stages. Results show that this modification gives more efficient exploration of the original algorithm. The second algorithm used a (Back propagation NN) algorithm. Finally a comparison between the results of two methods used were based on (NSL_KDD) data sets for the construction and evaluation of intrusion detection systems. This research is only interested in clustering the two categories (Normal and Abnormal) for the given connection records. Practices experiments result in intrude detection rate (99.183818%) for EPSO and intrude detection rate (69.446416%) for BP neural network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=IDS" title="IDS">IDS</a>, <a href="https://publications.waset.org/abstracts/search?q=SI" title=" SI"> SI</a>, <a href="https://publications.waset.org/abstracts/search?q=BP" title=" BP"> BP</a>, <a href="https://publications.waset.org/abstracts/search?q=NSL_KDD" title=" NSL_KDD"> NSL_KDD</a>, <a href="https://publications.waset.org/abstracts/search?q=PSO" title=" PSO"> PSO</a> </p> <a href="https://publications.waset.org/abstracts/26515/intrusion-detection-using-dual-artificial-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26515.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">382</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20037</span> An Intrusion Detection Systems Based on K-Means, K-Medoids and Support Vector Clustering Using Ensemble</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Mohammadpour">A. Mohammadpour</a>, <a href="https://publications.waset.org/abstracts/search?q=Ebrahim%20Najafi%20Kajabad"> Ebrahim Najafi Kajabad</a>, <a href="https://publications.waset.org/abstracts/search?q=Ghazale%20Ipakchi"> Ghazale Ipakchi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Presently, computer networks’ security rise in importance and many studies have also been conducted in this field. By the penetration of the internet networks in different fields, many things need to be done to provide a secure industrial and non-industrial network. Fire walls, appropriate Intrusion Detection Systems (IDS), encryption protocols for information sending and receiving, and use of authentication certificated are among things, which should be considered for system security. The aim of the present study is to use the outcome of several algorithms, which cause decline in IDS errors, in the way that improves system security and prevents additional overload to the system. Finally, regarding the obtained result we can also detect the amount and percentage of more sub attacks. By running the proposed system, which is based on the use of multi-algorithmic outcome and comparing that by the proposed single algorithmic methods, we observed a 78.64% result in attack detection that is improved by 3.14% than the proposed algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection%20systems" title="intrusion detection systems">intrusion detection systems</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=k-means" title=" k-means"> k-means</a>, <a href="https://publications.waset.org/abstracts/search?q=k-medoids" title=" k-medoids"> k-medoids</a>, <a href="https://publications.waset.org/abstracts/search?q=SV%20clustering" title=" SV clustering"> SV clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=ensemble" title=" ensemble"> ensemble</a> </p> <a href="https://publications.waset.org/abstracts/75738/an-intrusion-detection-systems-based-on-k-means-k-medoids-and-support-vector-clustering-using-ensemble" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75738.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">221</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20036</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/abstracts/search?q=Umar%20Albalawi">Umar Albalawi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sang%20C.%20Suh"> Sang C. Suh</a>, <a href="https://publications.waset.org/abstracts/search?q=Jinoh%20Kim"> Jinoh Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></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 class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection" title="intrusion detection">intrusion detection</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20learning" title=" supervised learning"> supervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20classification" title=" traffic classification"> traffic classification</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20networks" title=" computer networks"> computer networks</a> </p> <a href="https://publications.waset.org/abstracts/5421/incorporating-multiple-supervised-learning-algorithms-for-effective-intrusion-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5421.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">349</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20035</span> Intrusion Detection in SCADA Systems </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Leandros%20A.%20Maglaras">Leandros A. Maglaras</a>, <a href="https://publications.waset.org/abstracts/search?q=Jianmin%20Jiang"> Jianmin Jiang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The protection of the national infrastructures from cyberattacks is one of the main issues for national and international security. The funded European Framework-7 (FP7) research project CockpitCI introduces intelligent intrusion detection, analysis and protection techniques for Critical Infrastructures (CI). The paradox is that CIs massively rely on the newest interconnected and vulnerable Information and Communication Technology (ICT), whilst the control equipment, legacy software/hardware, is typically old. Such a combination of factors may lead to very dangerous situations, exposing systems to a wide variety of attacks. To overcome such threats, the CockpitCI project combines machine learning techniques with ICT technologies to produce advanced intrusion detection, analysis and reaction tools to provide intelligence to field equipment. This will allow the field equipment to perform local decisions in order to self-identify and self-react to abnormal situations introduced by cyberattacks. In this paper, an intrusion detection module capable of detecting malicious network traffic in a Supervisory Control and Data Acquisition (SCADA) system is presented. Malicious data in a SCADA system disrupt its correct functioning and tamper with its normal operation. OCSVM is an intrusion detection mechanism that does not need any labeled data for training or any information about the kind of anomaly is expecting for the detection process. This feature makes it ideal for processing SCADA environment data and automates SCADA performance monitoring. The OCSVM module developed is trained by network traces off line and detects anomalies in the system real time. The module is part of an IDS (intrusion detection system) developed under CockpitCI project and communicates with the other parts of the system by the exchange of IDMEF messages that carry information about the source of the incident, the time and a classification of the alarm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cyber-security" title="cyber-security">cyber-security</a>, <a href="https://publications.waset.org/abstracts/search?q=SCADA%20systems" title=" SCADA systems"> SCADA systems</a>, <a href="https://publications.waset.org/abstracts/search?q=OCSVM" title=" OCSVM"> OCSVM</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection" title=" intrusion detection"> intrusion detection</a> </p> <a href="https://publications.waset.org/abstracts/10165/intrusion-detection-in-scada-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10165.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">552</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20034</span> Applicability of Fuzzy Logic for Intrusion Detection in Mobile Adhoc Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ruchi%20Makani">Ruchi Makani</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20V.%20R.%20%20Reddy"> B. V. R. Reddy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mobile Adhoc Networks (MANETs) are gaining popularity due to their potential of providing low-cost mobile connectivity solutions to real-world communication problems. Integrating Intrusion Detection Systems (IDS) in MANETs is a tedious task by reason of its distinctive features such as dynamic topology, de-centralized authority and highly controlled/limited resource environment. IDS primarily use automated soft-computing techniques to monitor the inflow/outflow of traffic packets in a given network to detect intrusion. Use of machine learning techniques in IDS enables system to make decisions on intrusion while continuous keep learning about their dynamic environment. An appropriate IDS model is essential to be selected to expedite this application challenges. Thus, this paper focused on fuzzy-logic based machine learning IDS technique for MANETs and presented their applicability for achieving effectiveness in identifying the intrusions. Further, the selection of appropriate protocol attributes and fuzzy rules generation plays significant role for accuracy of the fuzzy-logic based IDS, have been discussed. This paper also presents the critical attributes of MANET’s routing protocol and its applicability in fuzzy logic based IDS. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AODV" title="AODV">AODV</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20adhoc%20networks" title=" mobile adhoc networks"> mobile adhoc networks</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection" title=" intrusion detection"> intrusion detection</a>, <a href="https://publications.waset.org/abstracts/search?q=anomaly%20detection" title=" anomaly detection"> anomaly detection</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20membership%20function" title=" fuzzy membership function"> fuzzy membership function</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20inference%20system" title=" fuzzy inference system"> fuzzy inference system</a> </p> <a href="https://publications.waset.org/abstracts/94205/applicability-of-fuzzy-logic-for-intrusion-detection-in-mobile-adhoc-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94205.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">177</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20033</span> Intrusion Detection In MANET Using Game Theory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20B.%20Kumbalavati">S. B. Kumbalavati</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20D.%20Mallapur"> J. D. Mallapur</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Y.%20Bendigeri"> K. Y. Bendigeri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A mobile Ad-hoc network (MANET) is a multihop wireless network where nodes communicate each other without any pre-deployed infrastructure. There is no central administrating unit. Hence, MANET is generally prone to many of the attacks. These attacks may alter, release or deny data. These attacks are nothing but intrusions. Intrusion is a set of actions that attempts to compromise integrity, confidentiality and availability of resources. A major issue in the design and operation of ad-hoc network is sharing the common spectrum or common channel bandwidth among all the nodes. We are performing intrusion detection using game theory approach. Game theory is a mathematical tool for analysing problems of competition and negotiation among the players in any field like marketing, e-commerce and networking. In this paper mathematical model is developed using game theory approach and intruders are detected and removed. Bandwidth utilization is estimated and comparison is made between bandwidth utilization with intrusion detection technique and without intrusion detection technique. Percentage of intruders and efficiency of the network is analysed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ad-hoc%20network" title="ad-hoc network">ad-hoc network</a>, <a href="https://publications.waset.org/abstracts/search?q=IDS" title=" IDS"> IDS</a>, <a href="https://publications.waset.org/abstracts/search?q=game%20theory" title=" game theory"> game theory</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor%20networks" title=" sensor networks"> sensor networks</a> </p> <a href="https://publications.waset.org/abstracts/6246/intrusion-detection-in-manet-using-game-theory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6246.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">387</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20032</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/abstracts/search?q=Zheni%20S.%20Stefanova">Zheni S. Stefanova</a>, <a href="https://publications.waset.org/abstracts/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/abstracts/search?q=cyber%20security" title="cyber security">cyber security</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20prevention" title=" intrusion prevention"> intrusion prevention</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20policy" title=" optimal policy"> optimal policy</a>, <a href="https://publications.waset.org/abstracts/search?q=Q-learning" title=" Q-learning"> Q-learning</a> </p> <a href="https://publications.waset.org/abstracts/88356/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/abstracts/88356.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">236</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20031</span> Detecting Venomous Files in IDS Using an Approach Based on Data Mining Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sukhleen%20Kaur">Sukhleen Kaur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In security groundwork, Intrusion Detection System (IDS) has become an important component. The IDS has received increasing attention in recent years. IDS is one of the effective way to detect different kinds of attacks and malicious codes in a network and help us to secure the network. Data mining techniques can be implemented to IDS, which analyses the large amount of data and gives better results. Data mining can contribute to improving intrusion detection by adding a level of focus to anomaly detection. So far the study has been carried out on finding the attacks but this paper detects the malicious files. Some intruders do not attack directly, but they hide some harmful code inside the files or may corrupt those file and attack the system. These files are detected according to some defined parameters which will form two lists of files as normal files and harmful files. After that data mining will be performed. In this paper a hybrid classifier has been used via Naive Bayes and Ripper classification methods. The results show how the uploaded file in the database will be tested against the parameters and then it is characterised as either normal or harmful file and after that the mining is performed. Moreover, when a user tries to mine on harmful file it will generate an exception that mining cannot be made on corrupted or harmful files. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=association" title=" association"> association</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection%20system" title=" intrusion detection system"> intrusion detection system</a>, <a href="https://publications.waset.org/abstracts/search?q=misuse%20detection" title=" misuse detection"> misuse detection</a>, <a href="https://publications.waset.org/abstracts/search?q=anomaly%20detection" title=" anomaly detection"> anomaly detection</a>, <a href="https://publications.waset.org/abstracts/search?q=naive%20Bayes" title=" naive Bayes"> naive Bayes</a>, <a href="https://publications.waset.org/abstracts/search?q=ripper" title=" ripper"> ripper</a> </p> <a href="https://publications.waset.org/abstracts/10822/detecting-venomous-files-in-ids-using-an-approach-based-on-data-mining-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10822.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">414</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20030</span> A Research and Application of Feature Selection Based on IWO and Tabu Search</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Laicheng%20Cao">Laicheng Cao</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiangqian%20Su"> Xiangqian Su</a>, <a href="https://publications.waset.org/abstracts/search?q=Youxiao%20Wu"> Youxiao Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Feature selection is one of the important problems in network security, pattern recognition, data mining and other fields. In order to remove redundant features, effectively improve the detection speed of intrusion detection system, proposes a new feature selection method, which is based on the invasive weed optimization (IWO) algorithm and tabu search algorithm(TS). Use IWO as a global search, tabu search algorithm for local search, to improve the results of IWO algorithm. The experimental results show that the feature selection method can effectively remove the redundant features of network data information in feature selection, reduction time, and to guarantee accurate detection rate, effectively improve the speed of detection system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection" title="intrusion detection">intrusion detection</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=iwo" title=" iwo"> iwo</a>, <a href="https://publications.waset.org/abstracts/search?q=tabu%20search" title=" tabu search"> tabu search</a> </p> <a href="https://publications.waset.org/abstracts/28884/a-research-and-application-of-feature-selection-based-on-iwo-and-tabu-search" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28884.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">530</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20029</span> A Proposed Optimized and Efficient Intrusion Detection System for Wireless Sensor Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdulaziz%20Alsadhan">Abdulaziz Alsadhan</a>, <a href="https://publications.waset.org/abstracts/search?q=Naveed%20Khan"> Naveed Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years intrusions on computer network are the major security threat. Hence, it is important to impede such intrusions. The hindrance of such intrusions entirely relies on its detection, which is primary concern of any security tool like Intrusion Detection System (IDS). Therefore, it is imperative to accurately detect network attack. Numerous intrusion detection techniques are available but the main issue is their performance. The performance of IDS can be improved by increasing the accurate detection rate and reducing false positive. The existing intrusion detection techniques have the limitation of usage of raw data set for classification. The classifier may get jumble due to redundancy, which results incorrect classification. To minimize this problem, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Local Binary Pattern (LBP) can be applied to transform raw features into principle features space and select the features based on their sensitivity. Eigen values can be used to determine the sensitivity. To further classify, the selected features greedy search, back elimination, and Particle Swarm Optimization (PSO) can be used to obtain a subset of features with optimal sensitivity and highest discriminatory power. These optimal feature subset used to perform classification. For classification purpose, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) used due to its proven ability in classification. The Knowledge Discovery and Data mining (KDD’99) cup dataset was considered as a benchmark for evaluating security detection mechanisms. The proposed approach can provide an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Particle%20Swarm%20Optimization%20%28PSO%29" title="Particle Swarm Optimization (PSO)">Particle Swarm Optimization (PSO)</a>, <a href="https://publications.waset.org/abstracts/search?q=Principle%20Component%20Analysis%20%28PCA%29" title=" Principle Component Analysis (PCA)"> Principle Component Analysis (PCA)</a>, <a href="https://publications.waset.org/abstracts/search?q=Linear%20Discriminant%20Analysis%20%28LDA%29" title=" Linear Discriminant Analysis (LDA)"> Linear Discriminant Analysis (LDA)</a>, <a href="https://publications.waset.org/abstracts/search?q=Local%20Binary%20Pattern%20%28LBP%29" title=" Local Binary Pattern (LBP)"> Local Binary Pattern (LBP)</a>, <a href="https://publications.waset.org/abstracts/search?q=Support%20Vector%20Machine%20%28SVM%29" title=" Support Vector Machine (SVM)"> Support Vector Machine (SVM)</a>, <a href="https://publications.waset.org/abstracts/search?q=Multilayer%20Perceptron%20%28MLP%29" title=" Multilayer Perceptron (MLP)"> Multilayer Perceptron (MLP)</a> </p> <a href="https://publications.waset.org/abstracts/1787/a-proposed-optimized-and-efficient-intrusion-detection-system-for-wireless-sensor-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1787.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">367</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20028</span> Analysis and Design Modeling for Next Generation Network Intrusion Detection and Prevention System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nareshkumar%20Harale">Nareshkumar Harale</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20B.%20Meshram"> B. B. Meshram</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The continued exponential growth of successful cyber intrusions against today’s businesses has made it abundantly clear that traditional perimeter security measures are no longer adequate and effective. We evolved the network trust architecture from trust-untrust to Zero-Trust, With Zero Trust, essential security capabilities are deployed in a way that provides policy enforcement and protection for all users, devices, applications, data resources, and the communications traffic between them, regardless of their location. Information exchange over the Internet, in spite of inclusion of advanced security controls, is always under innovative, inventive and prone to cyberattacks. TCP/IP protocol stack, the adapted standard for communication over network, suffers from inherent design vulnerabilities such as communication and session management protocols, routing protocols and security protocols are the major cause of major attacks. With the explosion of cyber security threats, such as viruses, worms, rootkits, malwares, Denial of Service attacks, accomplishing efficient and effective intrusion detection and prevention is become crucial and challenging too. In this paper, we propose a design and analysis model for next generation network intrusion detection and protection system as part of layered security strategy. The proposed system design provides intrusion detection for wide range of attacks with layered architecture and framework. The proposed network intrusion classification framework deals with cyberattacks on standard TCP/IP protocol, routing protocols and security protocols. It thereby forms the basis for detection of attack classes and applies signature based matching for known cyberattacks and data mining based machine learning approaches for unknown cyberattacks. Our proposed implemented software can effectively detect attacks even when malicious connections are hidden within normal events. The unsupervised learning algorithm applied to network audit data trails results in unknown intrusion detection. Association rule mining algorithms generate new rules from collected audit trail data resulting in increased intrusion prevention though integrated firewall systems. Intrusion response mechanisms can be initiated in real-time thereby minimizing the impact of network intrusions. Finally, we have shown that our approach can be validated and how the analysis results can be used for detecting and protection from the new network anomalies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=network%20intrusion%20detection" title="network intrusion detection">network intrusion detection</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20intrusion%20prevention" title=" network intrusion prevention"> network intrusion prevention</a>, <a href="https://publications.waset.org/abstracts/search?q=association%20rule%20mining" title=" association rule mining"> association rule mining</a>, <a href="https://publications.waset.org/abstracts/search?q=system%20analysis%20and%20design" title=" system analysis and design"> system analysis and design</a> </p> <a href="https://publications.waset.org/abstracts/56014/analysis-and-design-modeling-for-next-generation-network-intrusion-detection-and-prevention-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56014.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">227</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20027</span> An Immune-Inspired Web Defense Architecture</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Islam%20Khalil">Islam Khalil</a>, <a href="https://publications.waset.org/abstracts/search?q=Amr%20El-Kadi"> Amr El-Kadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the increased use of web technologies, microservices, and Application Programming Interface (API) for integration between systems, and with the development of containerization of services on the operating system level as a method of isolating system execution and for easing the deployment and scaling of systems, there is a growing need as well as opportunities for providing platforms that improve the security of such services. In our work, we propose an architecture for a containerization platform that utilizes various concepts derived from the human immune system. The goal of the proposed containerization platform is to introduce the concept of slowing down or throttling suspected malicious digital pathogens (intrusions) to reduce their damage footprint while providing more opportunities for forensic inspection of suspected pathogens in addition to the ability to snapshot, rollback, and recover from possible damage. The proposed platform also leverages existing intrusion detection algorithms by integrating and orchestrating their cooperative operation for more effective intrusion detection. We show how this model reduces the damage footprint of intrusions and gives a greater time window for forensic investigation. Moreover, during our experiments, our proposed platform was able to uncover unintentional system design flaws that resulted in internal DDoS-like attacks by submodules of the system itself rather than external intrusions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=containers" title="containers">containers</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20immunity" title=" human immunity"> human immunity</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection" title=" intrusion detection"> intrusion detection</a>, <a href="https://publications.waset.org/abstracts/search?q=security" title=" security"> security</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20services" title=" web services"> web services</a> </p> <a href="https://publications.waset.org/abstracts/166739/an-immune-inspired-web-defense-architecture" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/166739.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">95</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20026</span> Performance Analysis of Traffic Classification with Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Htay%20Htay%20Yi">Htay Htay Yi</a>, <a href="https://publications.waset.org/abstracts/search?q=Zin%20May%20Aye"> Zin May Aye</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Network security is role of the ICT environment because malicious users are continually growing that realm of education, business, and then related with ICT. The network security contravention is typically described and examined centrally based on a security event management system. The firewalls, Intrusion Detection System (IDS), and Intrusion Prevention System are becoming essential to monitor or prevent of potential violations, incidents attack, and imminent threats. In this system, the firewall rules are set only for where the system policies are needed. Dataset deployed in this system are derived from the testbed environment. The traffic as in DoS and PortScan traffics are applied in the testbed with firewall and IDS implementation. The network traffics are classified as normal or attacks in the existing testbed environment based on six machine learning classification methods applied in the system. It is required to be tested to get datasets and applied for DoS and PortScan. The dataset is based on CICIDS2017 and some features have been added. This system tested 26 features from the applied dataset. The system is to reduce false positive rates and to improve accuracy in the implemented testbed design. The system also proves good performance by selecting important features and comparing existing a dataset by machine learning classifiers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=false%20negative%20rate" title="false negative rate">false negative rate</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection%20system" title=" intrusion detection system"> intrusion detection system</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20methods" title=" machine learning methods"> machine learning methods</a>, <a href="https://publications.waset.org/abstracts/search?q=performance" title=" performance"> performance</a> </p> <a href="https://publications.waset.org/abstracts/133091/performance-analysis-of-traffic-classification-with-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133091.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">118</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20025</span> Hybrid Anomaly Detection Using Decision Tree and Support Vector Machine</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elham%20Serkani">Elham Serkani</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Gharaee%20Garakani"> Hossein Gharaee Garakani</a>, <a href="https://publications.waset.org/abstracts/search?q=Naser%20Mohammadzadeh"> Naser Mohammadzadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Elaheh%20Vaezpour"> Elaheh Vaezpour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Intrusion detection systems (IDS) are the main components of network security. These systems analyze the network events for intrusion detection. The design of an IDS is through the training of normal traffic data or attack. The methods of machine learning are the best ways to design IDSs. In the method presented in this article, the pruning algorithm of C5.0 decision tree is being used to reduce the features of traffic data used and training IDS by the least square vector algorithm (LS-SVM). Then, the remaining features are arranged according to the predictor importance criterion. The least important features are eliminated in the order. The remaining features of this stage, which have created the highest level of accuracy in LS-SVM, are selected as the final features. The features obtained, compared to other similar articles which have examined the selected features in the least squared support vector machine model, are better in the accuracy, true positive rate, and false positive. The results are tested by the UNSW-NB15 dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title="decision tree">decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection%20system" title=" intrusion detection system"> intrusion detection system</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a> </p> <a href="https://publications.waset.org/abstracts/90456/hybrid-anomaly-detection-using-decision-tree-and-support-vector-machine" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90456.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">265</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20024</span> Survey of Intrusion Detection Systems and Their Assessment of the Internet of Things</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=James%20Kaweesa">James Kaweesa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Internet of Things (IoT) has become a critical component of modern technology, enabling the connection of numerous devices to the internet. The interconnected nature of IoT devices, along with their heterogeneous and resource-constrained nature, makes them vulnerable to various types of attacks, such as malware, denial-of-service attacks, and network scanning. Intrusion Detection Systems (IDSs) are a key mechanism for protecting IoT networks and from attacks by identifying and alerting administrators to suspicious activities. In this review, the paper will discuss the different types of IDSs available for IoT systems and evaluate their effectiveness in detecting and preventing attacks. Also, examine the various evaluation methods used to assess the performance of IDSs and the challenges associated with evaluating them in IoT environments. The review will highlight the need for effective and efficient IDSs that can cope with the unique characteristics of IoT networks, including their heterogeneity, dynamic topology, and resource constraints. The paper will conclude by indicating where further research is needed to develop IDSs that can address these challenges and effectively protect IoT systems from cyber threats. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cyber-threats" title="cyber-threats">cyber-threats</a>, <a href="https://publications.waset.org/abstracts/search?q=iot" title=" iot"> iot</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection%20system" title=" intrusion detection system"> intrusion detection system</a>, <a href="https://publications.waset.org/abstracts/search?q=networks" title=" networks"> networks</a> </p> <a href="https://publications.waset.org/abstracts/164963/survey-of-intrusion-detection-systems-and-their-assessment-of-the-internet-of-things" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164963.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> 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