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attacks</title> <meta name="description" content="Search results for: malicious attacks"> <meta name="keywords" content="malicious attacks"> <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/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="malicious attacks"> <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> 661</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: malicious attacks</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">661</span> Study on Network-Based Technology for Detecting Potentially Malicious Websites</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Byung-Ik%20Kim">Byung-Ik Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Hong-Koo%20Kang"> Hong-Koo Kang</a>, <a href="https://publications.waset.org/abstracts/search?q=Tae-Jin%20Lee"> Tae-Jin Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Hae-Ryong%20Park"> Hae-Ryong Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cyber terrors against specific enterprises or countries have been increasing recently. Such attacks against specific targets are called advanced persistent threat (APT), and they are giving rise to serious social problems. The malicious behaviors of APT attacks mostly affect websites and penetrate enterprise networks to perform malevolent acts. Although many enterprises invest heavily in security to defend against such APT threats, they recognize the APT attacks only after the latter are already in action. This paper discusses the characteristics of APT attacks at each step as well as the strengths and weaknesses of existing malicious code detection technologies to check their suitability for detecting APT attacks. It then proposes a network-based malicious behavior detection algorithm to protect the enterprise or national networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Advanced%20Persistent%20Threat%20%28APT%29" title="Advanced Persistent Threat (APT)">Advanced Persistent Threat (APT)</a>, <a href="https://publications.waset.org/abstracts/search?q=malware" title=" malware"> malware</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20security" title=" network security"> network security</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20packet" title=" network packet"> network packet</a>, <a href="https://publications.waset.org/abstracts/search?q=exploit%20kits" title=" exploit kits"> exploit kits</a> </p> <a href="https://publications.waset.org/abstracts/2429/study-on-network-based-technology-for-detecting-potentially-malicious-websites" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2429.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">366</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">660</span> The Impact of Malicious Attacks on the Performance of Routing Protocols in Mobile Ad-Hoc Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Habib%20Gorine">Habib Gorine</a>, <a href="https://publications.waset.org/abstracts/search?q=Rabia%20Saleh"> Rabia Saleh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mobile Ad-Hoc Networks are the special type of wireless networks which share common security requirements with other networks such as confidentiality, integrity, authentication, and availability, which need to be addressed in order to secure data transfer through the network. Their routing protocols are vulnerable to various malicious attacks which could have a devastating consequence on data security. In this paper, three types of attacks such as selfish, gray hole, and black hole attacks have been applied to the two most important routing protocols in MANET named dynamic source routing and ad-hoc on demand distance vector in order to analyse and compare the impact of these attacks on the Network performance in terms of throughput, average delay, packet loss, and consumption of energy using NS2 simulator. <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=wireless%20networks" title=" wireless networks"> wireless networks</a>, <a href="https://publications.waset.org/abstracts/search?q=routing%20protocols" title=" routing protocols"> routing protocols</a>, <a href="https://publications.waset.org/abstracts/search?q=malicious%20attacks" title=" malicious attacks"> malicious attacks</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20networks%20simulation" title=" wireless networks simulation"> wireless networks simulation</a> </p> <a href="https://publications.waset.org/abstracts/88341/the-impact-of-malicious-attacks-on-the-performance-of-routing-protocols-in-mobile-ad-hoc-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88341.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">659</span> Cryptocurrency Crime: Behaviors of Malicious Smart Contracts in Blockchain</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Malaw%20Ndiaye">Malaw Ndiaye</a>, <a href="https://publications.waset.org/abstracts/search?q=Karim%20Konate"> Karim Konate</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Blockchain and smart contracts can be used to facilitate almost any financial transaction. Thanks to these smart contracts, the settlement of dividends and coupons could be automated. The blockchain would allow all these transactions to be saved in a single ledger rather than in many databases through many organizations as is currently the case. Smart contracts have become lucrative and profitable targets for attackers because they can hold a large amount of money. This paper takes stock of cryptocurrency crime by assessing attacks due to smart contracts and the cost of losses. These losses are often the result of two types of malicious contracts: vulnerable contracts and criminal smart contracts. Studying the behavior of malicious contracts allows us to understand the root causes and consequences of attacks and the defense capabilities that exist although they do not definitively solve the crime problem. It makes it possible to approach new defense perspectives which will be concretized in future work. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blockchain" title="blockchain">blockchain</a>, <a href="https://publications.waset.org/abstracts/search?q=malicious%20smart%20contracts" title=" malicious smart contracts"> malicious smart contracts</a>, <a href="https://publications.waset.org/abstracts/search?q=crypto-currency" title=" crypto-currency"> crypto-currency</a>, <a href="https://publications.waset.org/abstracts/search?q=crimes" title=" crimes"> crimes</a>, <a href="https://publications.waset.org/abstracts/search?q=attacks" title=" attacks"> attacks</a> </p> <a href="https://publications.waset.org/abstracts/135277/cryptocurrency-crime-behaviors-of-malicious-smart-contracts-in-blockchain" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135277.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">275</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">658</span> Quick Reference: Cyber Attacks Awareness and Prevention Method for Home Users</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Haydar%20Teymourlouei">Haydar Teymourlouei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is important to take security measures to protect your computer information, reduce identify theft, and prevent from malicious cyber-attacks. With cyber-attacks on the continuous rise, people need to understand and learn ways to prevent from these attacks. Cyber-attack is an important factor to be considered if one is to be able to protect oneself from malicious attacks. Without proper security measures, most computer technology would hinder home users more than such technologies would help. Knowledge of how cyber-attacks operate and protective steps that can be taken to reduce chances of its occurrence are key to increasing these security measures. The purpose of this paper is to inform home users on the importance of identifying and taking preventive steps to avoid cyberattacks. Throughout this paper, many aspects of cyber-attacks will be discuss: what a cyber-attack is, the affects of cyber-attack for home users, different types of cyber-attacks, methodology to prevent such attacks; home users can take to fortify security of their computer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cyber-attacks" title="cyber-attacks">cyber-attacks</a>, <a href="https://publications.waset.org/abstracts/search?q=home%20user" title=" home user"> home user</a>, <a href="https://publications.waset.org/abstracts/search?q=prevention" title=" prevention"> prevention</a>, <a href="https://publications.waset.org/abstracts/search?q=security" title=" security"> security</a>, <a href="https://publications.waset.org/abstracts/search?q=technology" title=" technology"> technology</a> </p> <a href="https://publications.waset.org/abstracts/25329/quick-reference-cyber-attacks-awareness-and-prevention-method-for-home-users" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25329.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">396</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">657</span> A Pattern Recognition Neural Network Model for Detection and Classification of SQL Injection Attacks </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Naghmeh%20Moradpoor%20Sheykhkanloo">Naghmeh Moradpoor Sheykhkanloo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Structured Query Language Injection (SQLI) attack is a code injection technique in which malicious SQL statements are inserted into a given SQL database by simply using a web browser. Losing data, disclosing confidential information or even changing the value of data are the severe damages that SQLI attack can cause on a given database. SQLI attack has also been rated as the number-one attack among top ten web application threats on Open Web Application Security Project (OWASP). OWASP is an open community dedicated to enabling organisations to consider, develop, obtain, function, and preserve applications that can be trusted. In this paper, we propose an effective pattern recognition neural network model for detection and classification of SQLI attacks. The proposed model is built from three main elements of: a Uniform Resource Locator (URL) generator in order to generate thousands of malicious and benign URLs, a URL classifier in order to: 1) classify each generated URL to either a benign URL or a malicious URL and 2) classify the malicious URLs into different SQLI attack categories, and an NN model in order to: 1) detect either a given URL is a malicious URL or a benign URL and 2) identify the type of SQLI attack for each malicious URL. The model is first trained and then evaluated by employing thousands of benign and malicious URLs. The results of the experiments are presented in order to demonstrate the effectiveness of the proposed approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title="neural networks">neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=SQL%20injection%20attacks" title=" SQL injection attacks"> SQL injection attacks</a>, <a href="https://publications.waset.org/abstracts/search?q=SQL%20injection%20attack%20classification" title=" SQL injection attack classification"> SQL injection attack classification</a>, <a href="https://publications.waset.org/abstracts/search?q=SQL%20injection%20attack%20detection" title=" SQL injection attack detection "> SQL injection attack detection </a> </p> <a href="https://publications.waset.org/abstracts/22997/a-pattern-recognition-neural-network-model-for-detection-and-classification-of-sql-injection-attacks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22997.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">469</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">656</span> Improving Cryptographically Generated Address Algorithm in IPv6 Secure Neighbor Discovery Protocol through Trust Management </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Moslehpour">M. Moslehpour</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Khorsandi"> S. Khorsandi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As transition to widespread use of IPv6 addresses has gained momentum, it has been shown to be vulnerable to certain security attacks such as those targeting Neighbor Discovery Protocol (NDP) which provides the address resolution functionality in IPv6. To protect this protocol, Secure Neighbor Discovery (SEND) is introduced. This protocol uses Cryptographically Generated Address (CGA) and asymmetric cryptography as a defense against threats on integrity and identity of NDP. Although SEND protects NDP against attacks, it is computationally intensive due to Hash2 condition in CGA. To improve the CGA computation speed, we parallelized CGA generation process and used the available resources in a trusted network. Furthermore, we focused on the influence of the existence of malicious nodes on the overall load of un-malicious ones in the network. According to the evaluation results, malicious nodes have adverse impacts on the average CGA generation time and on the average number of tries. We utilized a Trust Management that is capable of detecting and isolating the malicious node to remove possible incentives for malicious behavior. We have demonstrated the effectiveness of the Trust Management System in detecting the malicious nodes and hence improving the overall system performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CGA" title="CGA">CGA</a>, <a href="https://publications.waset.org/abstracts/search?q=ICMPv6" title=" ICMPv6"> ICMPv6</a>, <a href="https://publications.waset.org/abstracts/search?q=IPv6" title=" IPv6"> IPv6</a>, <a href="https://publications.waset.org/abstracts/search?q=malicious%20node" title=" malicious node"> malicious node</a>, <a href="https://publications.waset.org/abstracts/search?q=modifier" title=" modifier"> modifier</a>, <a href="https://publications.waset.org/abstracts/search?q=NDP" title=" NDP"> NDP</a>, <a href="https://publications.waset.org/abstracts/search?q=overall%20load" title=" overall load"> overall load</a>, <a href="https://publications.waset.org/abstracts/search?q=SEND" title=" SEND"> SEND</a>, <a href="https://publications.waset.org/abstracts/search?q=trust%20management" title=" trust management"> trust management</a> </p> <a href="https://publications.waset.org/abstracts/41739/improving-cryptographically-generated-address-algorithm-in-ipv6-secure-neighbor-discovery-protocol-through-trust-management" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41739.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">184</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">655</span> Survey Based Data Security Evaluation in Pakistan Financial Institutions against Malicious Attacks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Naveed%20Ghani">Naveed Ghani</a>, <a href="https://publications.waset.org/abstracts/search?q=Samreen%20Javed"> Samreen Javed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In today’s heterogeneous network environment, there is a growing demand for distrust clients to jointly execute secure network to prevent from malicious attacks as the defining task of propagating malicious code is to locate new targets to attack. Residual risk is always there no matter what solutions are implemented or whet so ever security methodology or standards being adapted. Security is the first and crucial phase in the field of Computer Science. The main aim of the Computer Security is gathering of information with secure network. No one need wonder what all that malware is trying to do: It's trying to steal money through data theft, bank transfers, stolen passwords, or swiped identities. From there, with the help of our survey we learn about the importance of white listing, antimalware programs, security patches, log files, honey pots, and more used in banks for financial data protection but there’s also a need of implementing the IPV6 tunneling with Crypto data transformation according to the requirements of new technology to prevent the organization from new Malware attacks and crafting of its own messages and sending them to the target. In this paper the writer has given the idea of implementing IPV6 Tunneling Secessions on private data transmission from financial organizations whose secrecy needed to be safeguarded. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=network%20worms" title="network worms">network worms</a>, <a href="https://publications.waset.org/abstracts/search?q=malware%20infection%20propagating%20malicious%20code" title=" malware infection propagating malicious code"> malware infection propagating malicious code</a>, <a href="https://publications.waset.org/abstracts/search?q=virus" title=" virus"> virus</a>, <a href="https://publications.waset.org/abstracts/search?q=security" title=" security"> security</a>, <a href="https://publications.waset.org/abstracts/search?q=VPN" title=" VPN"> VPN</a> </p> <a href="https://publications.waset.org/abstracts/2550/survey-based-data-security-evaluation-in-pakistan-financial-institutions-against-malicious-attacks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2550.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">358</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">654</span> Survey on Malware Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Doaa%20Wael">Doaa Wael</a>, <a href="https://publications.waset.org/abstracts/search?q=Naswa%20Abdelbaky"> Naswa Abdelbaky</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Malware is malicious software that is built to cause destructive actions and damage information systems and networks. Malware infections increase rapidly, and types of malware have become more sophisticated, which makes the malware detection process more difficult. On the other side, the Internet of Things IoT technology is vulnerable to malware attacks. These IoT devices are always connected to the internet and lack security. This makes them easy for hackers to access. These malware attacks are becoming the go-to attack for hackers. Thus, in order to deal with this challenge, new malware detection techniques are needed. Currently, building a blockchain solution that allows IoT devices to download any file from the internet and to verify/approve whether it is malicious or not is the need of the hour. In recent years, blockchain technology has stood as a solution to everything due to its features like decentralization, persistence, and anonymity. Moreover, using blockchain technology overcomes some difficulties in malware detection and improves the malware detection ratio over-than the techniques that do not utilize blockchain technology. In this paper, we study malware detection models which are based on blockchain technology. Furthermore, we elaborate on the effect of blockchain technology in malware detection, especially in the android environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=malware%20analysis" title="malware analysis">malware analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=blockchain" title=" blockchain"> blockchain</a>, <a href="https://publications.waset.org/abstracts/search?q=malware%20attacks" title=" malware attacks"> malware attacks</a>, <a href="https://publications.waset.org/abstracts/search?q=malware%20detection%20approaches" title=" malware detection approaches"> malware detection approaches</a> </p> <a href="https://publications.waset.org/abstracts/164823/survey-on-malware-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164823.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">87</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">653</span> A Distributed Cryptographically Generated Address Computing Algorithm for Secure Neighbor Discovery Protocol in IPv6</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Moslehpour">M. Moslehpour</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Khorsandi"> S. Khorsandi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to shortage in IPv4 addresses, transition to IPv6 has gained significant momentum in recent years. Like Address Resolution Protocol (ARP) in IPv4, Neighbor Discovery Protocol (NDP) provides some functions like address resolution in IPv6. Besides functionality of NDP, it is vulnerable to some attacks. To mitigate these attacks, Internet Protocol Security (IPsec) was introduced, but it was not efficient due to its limitation. Therefore, SEND protocol is proposed to automatic protection of auto-configuration process. It is secure neighbor discovery and address resolution process. To defend against threats on NDP’s integrity and identity, Cryptographically Generated Address (CGA) and asymmetric cryptography are used by SEND. Besides advantages of SEND, its disadvantages like the computation process of CGA algorithm and sequentially of CGA generation algorithm are considerable. In this paper, we parallel this process between network resources in order to improve it. In addition, we compare the CGA generation time in self-computing and distributed-computing process. We focus on the impact of the malicious nodes on the CGA generation time in the network. According to the result, although malicious nodes participate in the generation process, CGA generation time is less than when it is computed in a one-way. By Trust Management System, detecting and insulating malicious nodes is easier. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=NDP" title="NDP">NDP</a>, <a href="https://publications.waset.org/abstracts/search?q=IPsec" title=" IPsec"> IPsec</a>, <a href="https://publications.waset.org/abstracts/search?q=SEND" title=" SEND"> SEND</a>, <a href="https://publications.waset.org/abstracts/search?q=CGA" title=" CGA"> CGA</a>, <a href="https://publications.waset.org/abstracts/search?q=modifier" title=" modifier"> modifier</a>, <a href="https://publications.waset.org/abstracts/search?q=malicious%20node" title=" malicious node"> malicious node</a>, <a href="https://publications.waset.org/abstracts/search?q=self-computing" title=" self-computing"> self-computing</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed-computing" title=" distributed-computing"> distributed-computing</a> </p> <a href="https://publications.waset.org/abstracts/45747/a-distributed-cryptographically-generated-address-computing-algorithm-for-secure-neighbor-discovery-protocol-in-ipv6" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45747.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">278</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">652</span> An Entropy Based Novel Algorithm for Internal Attack Detection in Wireless Sensor Network </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20R.%20Ahmed">Muhammad R. Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Aseeri"> Mohammed Aseeri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wireless Sensor Network (WSN) consists of low-cost and multi functional resources constrain nodes that communicate at short distances through wireless links. It is open media and underpinned by an application driven technology for information gathering and processing. It can be used for many different applications range from military implementation in the battlefield, environmental monitoring, health sector as well as emergency response of surveillance. With its nature and application scenario, security of WSN had drawn a great attention. It is known to be valuable to variety of attacks for the construction of nodes and distributed network infrastructure. In order to ensure its functionality especially in malicious environments, security mechanisms are essential. Malicious or internal attacker has gained prominence and poses the most challenging attacks to WSN. Many works have been done to secure WSN from internal attacks but most of it relay on either training data set or predefined threshold. Without a fixed security infrastructure a WSN needs to find the internal attacks is a challenge. In this paper we present an internal attack detection method based on maximum entropy model. The final experimental works showed that the proposed algorithm does work well at the designed level. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=internal%20attack" title="internal attack">internal attack</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20sensor%20network" title=" wireless sensor network"> wireless sensor network</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20security" title=" network security"> network security</a>, <a href="https://publications.waset.org/abstracts/search?q=entropy" title=" entropy"> entropy</a> </p> <a href="https://publications.waset.org/abstracts/26980/an-entropy-based-novel-algorithm-for-internal-attack-detection-in-wireless-sensor-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26980.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">455</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">651</span> Taxonomy of Threats and Vulnerabilities in Smart Grid Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Faisal%20Al%20Yahmadi">Faisal Al Yahmadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20R.%20Ahmed"> Muhammad R. Ahmed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electric power is a fundamental necessity in the 21<sup>st</sup> century. Consequently, any break in electric power is probably going to affect the general activity. To make the power supply smooth and efficient, a smart grid network is introduced which uses communication technology. In any communication network, security is essential. It has been observed from several recent incidents that adversary causes an interruption to the operation of networks. In order to resolve the issues, it is vital to understand the threats and vulnerabilities associated with the smart grid networks. In this paper, we have investigated the threats and vulnerabilities in Smart Grid Networks (SGN) and the few solutions in the literature. Proposed solutions showed developments in electricity theft countermeasures, Denial of services attacks (DoS) and malicious injection attacks detection model, as well as malicious nodes detection using watchdog like techniques and other solutions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=smart%20grid%20network" title="smart grid network">smart grid network</a>, <a href="https://publications.waset.org/abstracts/search?q=security" title=" security"> security</a>, <a href="https://publications.waset.org/abstracts/search?q=threats" title=" threats"> threats</a>, <a href="https://publications.waset.org/abstracts/search?q=vulnerabilities" title=" vulnerabilities"> vulnerabilities</a> </p> <a href="https://publications.waset.org/abstracts/135866/taxonomy-of-threats-and-vulnerabilities-in-smart-grid-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135866.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">139</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">650</span> Exploring Cybersecurity and Phishing Attacks within Healthcare Institutions in Saudi Arabia: A Narrative Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ebtesam%20Shadadi">Ebtesam Shadadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Rasha%20Ibrahim"> Rasha Ibrahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Essam%20Ghadafi"> Essam Ghadafi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Phishing poses a significant threat as a cybercrime by tricking end users into revealing their confidential and sensitive information. Attackers often manipulate victims to achieve their malicious goals. The increasing prevalence of Phishing has led to extensive research on this issue, including studies focusing on phishing attempts in healthcare institutions in the Kingdom of Saudi Arabia. This paper explores the importance of analyzing phishing attacks, specifically focusing on those targeting the healthcare industry. The study delves into the tactics, obstacles, and remedies associated with these attacks, all while considering the implications for Saudi Vision 2030. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=phishing" title="phishing">phishing</a>, <a href="https://publications.waset.org/abstracts/search?q=cybersecurity" title=" cybersecurity"> cybersecurity</a>, <a href="https://publications.waset.org/abstracts/search?q=cyber%20threat" title=" cyber threat"> cyber threat</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20engineering" title=" social engineering"> social engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=vision%202030" title=" vision 2030"> vision 2030</a> </p> <a href="https://publications.waset.org/abstracts/186544/exploring-cybersecurity-and-phishing-attacks-within-healthcare-institutions-in-saudi-arabia-a-narrative-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186544.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">61</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">649</span> Ensuring Cyber Security Using Kippo Honeypots</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Vivekananda%20Pandian">S. Vivekananda Pandian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A major challenging task in this current scenario is protecting your computer and other electronic gadgets against Cyber-attacks. In this current era Cyber warfare becomes a major threat to the entire world which targets a particular organization or a country spreading the Malwares, Breaching the securities, causing major loss to the organization. Several sectors both public and private are computerized such as Energy sectors, Oil refinery sectors, Defense sectors and Aviation sectors are prone to attacks. Several attacks are unknown while accessing the internet. To study the characteristics and Intention of the Attacker Kippo Honeypots are used. Honeypots are the trap set by us which enables them to monitor the malicious activities and detailed study about attackers which leads to strengthening of the security. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=attackers" title="attackers">attackers</a>, <a href="https://publications.waset.org/abstracts/search?q=security" title=" security"> security</a>, <a href="https://publications.waset.org/abstracts/search?q=Kippo%20Honeypots" title=" Kippo Honeypots"> Kippo Honeypots</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20machine" title=" virtual machine "> virtual machine </a> </p> <a href="https://publications.waset.org/abstracts/23806/ensuring-cyber-security-using-kippo-honeypots" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23806.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">427</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">648</span> Deep Learning and Accurate Performance Measure Processes for Cyber Attack Detection among Web Logs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Noureddine%20Mohtaram">Noureddine Mohtaram</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeremy%20Patrix"> Jeremy Patrix</a>, <a href="https://publications.waset.org/abstracts/search?q=Jerome%20Verny"> Jerome Verny</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As an enormous number of online services have been developed into web applications, security problems based on web applications are becoming more serious now. Most intrusion detection systems rely on each request to find the cyber-attack rather than on user behavior, and these systems can only protect web applications against known vulnerabilities rather than certain zero-day attacks. In order to detect new attacks, we analyze the HTTP protocols of web servers to divide them into two categories: normal attacks and malicious attacks. On the other hand, the quality of the results obtained by deep learning (DL) in various areas of big data has given an important motivation to apply it to cybersecurity. Deep learning for attack detection in cybersecurity has the potential to be a robust tool from small transformations to new attacks due to its capability to extract more high-level features. This research aims to take a new approach, deep learning to cybersecurity, to classify these two categories to eliminate attacks and protect web servers of the defense sector which encounters different web traffic compared to other sectors (such as e-commerce, web app, etc.). The result shows that by using a machine learning method, a higher accuracy rate, and a lower false alarm detection rate can be achieved. <p class="card-text"><strong>Keywords:</strong> <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=HTTP%20protocol" title=" HTTP protocol"> HTTP protocol</a>, <a href="https://publications.waset.org/abstracts/search?q=logs" title=" logs"> logs</a>, <a href="https://publications.waset.org/abstracts/search?q=cyber%20attack" title=" cyber attack"> cyber attack</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/136582/deep-learning-and-accurate-performance-measure-processes-for-cyber-attack-detection-among-web-logs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136582.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">211</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">647</span> Deep Reinforcement Learning and Generative Adversarial Networks Approach to Thwart Intrusions and Adversarial Attacks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fabrice%20Setephin%20Atedjio">Fabrice Setephin Atedjio</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean-Pierre%20Lienou"> Jean-Pierre Lienou</a>, <a href="https://publications.waset.org/abstracts/search?q=Frederica%20F.%20Nelson"> Frederica F. Nelson</a>, <a href="https://publications.waset.org/abstracts/search?q=Sachin%20S.%20Shetty"> Sachin S. Shetty</a>, <a href="https://publications.waset.org/abstracts/search?q=Charles%20A.%20Kamhoua"> Charles A. Kamhoua</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Malicious users exploit vulnerabilities in computer systems, significantly disrupting their performance and revealing the inadequacies of existing protective solutions. Even machine learning-based approaches, designed to ensure reliability, can be compromised by adversarial attacks that undermine their robustness. This paper addresses two critical aspects of enhancing model reliability. First, we focus on improving model performance and robustness against adversarial threats. To achieve this, we propose a strategy by harnessing deep reinforcement learning. Second, we introduce an approach leveraging generative adversarial networks to counter adversarial attacks effectively. Our results demonstrate substantial improvements over previous works in the literature, with classifiers exhibiting enhanced accuracy in classification tasks, even in the presence of adversarial perturbations. These findings underscore the efficacy of the proposed model in mitigating intrusions and adversarial attacks within the machine-learning landscape. <p class="card-text"><strong>Keywords:</strong> <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=reliability" title=" reliability"> reliability</a>, <a href="https://publications.waset.org/abstracts/search?q=adversarial%20attacks" title=" adversarial attacks"> adversarial attacks</a>, <a href="https://publications.waset.org/abstracts/search?q=deep-reinforcement%20learning" title=" deep-reinforcement learning"> deep-reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=robustness" title=" robustness"> robustness</a> </p> <a href="https://publications.waset.org/abstracts/194008/deep-reinforcement-learning-and-generative-adversarial-networks-approach-to-thwart-intrusions-and-adversarial-attacks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/194008.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">9</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">646</span> Two-Level Graph Causality to Detect and Predict Random Cyber-Attacks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Van%20Trieu">Van Trieu</a>, <a href="https://publications.waset.org/abstracts/search?q=Shouhuai%20Xu"> Shouhuai Xu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yusheng%20Feng"> Yusheng Feng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Tracking attack trajectories can be difficult, with limited information about the nature of the attack. Even more difficult as attack information is collected by Intrusion Detection Systems (IDSs) due to the current IDSs having some limitations in identifying malicious and anomalous traffic. Moreover, IDSs only point out the suspicious events but do not show how the events relate to each other or which event possibly cause the other event to happen. Because of this, it is important to investigate new methods capable of performing the tracking of attack trajectories task quickly with less attack information and dependency on IDSs, in order to prioritize actions during incident responses. This paper proposes a two-level graph causality framework for tracking attack trajectories in internet networks by leveraging observable malicious behaviors to detect what is the most probable attack events that can cause another event to occur in the system. Technically, given the time series of malicious events, the framework extracts events with useful features, such as attack time and port number, to apply to the conditional independent tests to detect the relationship between attack events. Using the academic datasets collected by IDSs, experimental results show that the framework can quickly detect the causal pairs that offer meaningful insights into the nature of the internet network, given only reasonable restrictions on network size and structure. Without the framework’s guidance, these insights would not be able to discover by the existing tools, such as IDSs. It would cost expert human analysts a significant time if possible. The computational results from the proposed two-level graph network model reveal the obvious pattern and trends. In fact, more than 85% of causal pairs have the average time difference between the causal and effect events in both computed and observed data within 5 minutes. This result can be used as a preventive measure against future attacks. Although the forecast may be short, from 0.24 seconds to 5 minutes, it is long enough to be used to design a prevention protocol to block those attacks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=causality" title="causality">causality</a>, <a href="https://publications.waset.org/abstracts/search?q=multilevel%20graph" title=" multilevel graph"> multilevel graph</a>, <a href="https://publications.waset.org/abstracts/search?q=cyber-attacks" title=" cyber-attacks"> cyber-attacks</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a> </p> <a href="https://publications.waset.org/abstracts/146227/two-level-graph-causality-to-detect-and-predict-random-cyber-attacks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146227.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">156</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">645</span> To Ensure Maximum Voter Privacy in E-Voting Using Blockchain, Convolutional Neural Network, and Quantum Key Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bhaumik%20Tyagi">Bhaumik Tyagi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mandeep%20Kaur"> Mandeep Kaur</a>, <a href="https://publications.waset.org/abstracts/search?q=Kanika%20Singla"> Kanika Singla</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The advancement of blockchain has facilitated scholars to remodel e-voting systems for future generations. Server-side attacks like SQL injection attacks and DOS attacks are the most common attacks nowadays, where malicious codes are injected into the system through user input fields by illicit users, which leads to data leakage in the worst scenarios. Besides, quantum attacks are also there which manipulate the transactional data. In order to deal with all the above-mentioned attacks, integration of blockchain, convolutional neural network (CNN), and Quantum Key Distribution is done in this very research. The utilization of blockchain technology in e-voting applications is not a novel concept. But privacy and security issues are still there in a public and private blockchains. To solve this, the use of a hybrid blockchain is done in this research. This research proposed cryptographic signatures and blockchain algorithms to validate the origin and integrity of the votes. The convolutional neural network (CNN), a normalized version of the multilayer perceptron, is also applied in the system to analyze visual descriptions upon registration in a direction to enhance the privacy of voters and the e-voting system. Quantum Key Distribution is being implemented in order to secure a blockchain-based e-voting system from quantum attacks using quantum algorithms. Implementation of e-voting blockchain D-app and providing a proposed solution for the privacy of voters in e-voting using Blockchain, CNN, and Quantum Key Distribution is done. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hybrid%20blockchain" title="hybrid blockchain">hybrid blockchain</a>, <a href="https://publications.waset.org/abstracts/search?q=secure%20e-voting%20system" title=" secure e-voting system"> secure e-voting system</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=quantum%20key%20distribution" title=" quantum key distribution"> quantum key distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=one-time%20pad" title=" one-time pad"> one-time pad</a> </p> <a href="https://publications.waset.org/abstracts/160604/to-ensure-maximum-voter-privacy-in-e-voting-using-blockchain-convolutional-neural-network-and-quantum-key-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160604.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">94</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">644</span> USBware: A Trusted and Multidisciplinary Framework for Enhanced Detection of USB-Based Attacks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nir%20Nissim">Nir Nissim</a>, <a href="https://publications.waset.org/abstracts/search?q=Ran%20Yahalom"> Ran Yahalom</a>, <a href="https://publications.waset.org/abstracts/search?q=Tomer%20Lancewiki"> Tomer Lancewiki</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuval%20Elovici"> Yuval Elovici</a>, <a href="https://publications.waset.org/abstracts/search?q=Boaz%20Lerner"> Boaz Lerner</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Attackers increasingly take advantage of innocent users who tend to use USB devices casually, assuming these devices benign when in fact they may carry an embedded malicious behavior or hidden malware. USB devices have many properties and capabilities that have become the subject of malicious operations. Many of the recent attacks targeting individuals, and especially organizations, utilize popular and widely used USB devices, such as mice, keyboards, flash drives, printers, and smartphones. However, current detection tools, techniques, and solutions generally fail to detect both the known and unknown attacks launched via USB devices. Significance: We propose USBWARE, a project that focuses on the vulnerabilities of USB devices and centers on the development of a comprehensive detection framework that relies upon a crucial attack repository. USBWARE will allow researchers and companies to better understand the vulnerabilities and attacks associated with USB devices as well as providing a comprehensive platform for developing detection solutions. Methodology: The framework of USBWARE is aimed at accurate detection of both known and unknown USB-based attacks by a process that efficiently enhances the framework's detection capabilities over time. The framework will integrate two main security approaches in order to enhance the detection of USB-based attacks associated with a variety of USB devices. The first approach is aimed at the detection of known attacks and their variants, whereas the second approach focuses on the detection of unknown attacks. USBWARE will consist of six independent but complimentary detection modules, each detecting attacks based on a different approach or discipline. These modules include novel ideas and algorithms inspired from or already developed within our team's domains of expertise, including cyber security, electrical and signal processing, machine learning, and computational biology. The establishment and maintenance of the USBWARE’s dynamic and up-to-date attack repository will strengthen the capabilities of the USBWARE detection framework. The attack repository’s infrastructure will enable researchers to record, document, create, and simulate existing and new USB-based attacks. This data will be used to maintain the detection framework’s updatability by incorporating knowledge regarding new attacks. Based on our experience in the cyber security domain, we aim to design the USBWARE framework so that it will have several characteristics that are crucial for this type of cyber-security detection solution. Specifically, the USBWARE framework should be: Novel, Multidisciplinary, Trusted, Lightweight, Extendable, Modular and Updatable and Adaptable. Major Findings: Based on our initial survey, we have already found more than 23 types of USB-based attacks, divided into six major categories. Our preliminary evaluation and proof of concepts showed that our detection modules can be used for efficient detection of several basic known USB attacks. Further research, development, and enhancements are required so that USBWARE will be capable to cover all of the major known USB attacks and to detect unknown attacks. Conclusion: USBWARE is a crucial detection framework that must be further enhanced and developed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=USB" title="USB">USB</a>, <a href="https://publications.waset.org/abstracts/search?q=device" title=" device"> device</a>, <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=attack" title=" attack"> attack</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a> </p> <a href="https://publications.waset.org/abstracts/50734/usbware-a-trusted-and-multidisciplinary-framework-for-enhanced-detection-of-usb-based-attacks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50734.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">398</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">643</span> ANOVA-Based Feature Selection and Machine Learning System for IoT Anomaly Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Ali">Muhammad Ali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cyber-attacks and anomaly detection on the Internet of Things (IoT) infrastructure is emerging concern in the domain of data-driven intrusion. Rapidly increasing IoT risk is now making headlines around the world. denial of service, malicious control, data type probing, malicious operation, DDos, scan, spying, and wrong setup are attacks and anomalies that can affect an IoT system failure. Everyone talks about cyber security, connectivity, smart devices, and real-time data extraction. IoT devices expose a wide variety of new cyber security attack vectors in network traffic. For further than IoT development, and mainly for smart and IoT applications, there is a necessity for intelligent processing and analysis of data. So, our approach is too secure. We train several machine learning models that have been compared to accurately predicting attacks and anomalies on IoT systems, considering IoT applications, with ANOVA-based feature selection with fewer prediction models to evaluate network traffic to help prevent IoT devices. The machine learning (ML) algorithms that have been used here are KNN, SVM, NB, D.T., and R.F., with the most satisfactory test accuracy with fast detection. The evaluation of ML metrics includes precision, recall, F1 score, FPR, NPV, G.M., MCC, and AUC & ROC. The Random Forest algorithm achieved the best results with less prediction time, with an accuracy of 99.98%. <p class="card-text"><strong>Keywords:</strong> <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=analysis%20of%20variance" title=" analysis of variance"> analysis of variance</a>, <a href="https://publications.waset.org/abstracts/search?q=Internet%20of%20Thing" title=" Internet of Thing"> Internet of Thing</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20security" title=" network security"> network security</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/152701/anova-based-feature-selection-and-machine-learning-system-for-iot-anomaly-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152701.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">125</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">642</span> Ontology for Cross-Site-Scripting (XSS) Attack in Cybersecurity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jean%20Rosemond%20Dora">Jean Rosemond Dora</a>, <a href="https://publications.waset.org/abstracts/search?q=Karol%20Nemoga"> Karol Nemoga</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we tackle a frequent problem that frequently occurs in the cybersecurity field which is the exploitation of websites by XSS attacks, which are nowadays considered a complicated attack. These types of attacks aim to execute malicious scripts in a web browser of the client by including code in a legitimate web page. A serious matter is when a website accepts the “user-input” option. Attackers can exploit the web application (if vulnerable), and then steal sensitive data (session cookies, passwords, credit cards, etc.) from the server and/or from the client. However, the difficulty of the exploitation varies from website to website. Our focus is on the usage of ontology in cybersecurity against XSS attacks, on the importance of the ontology, and its core meaning for cybersecurity. We explain how a vulnerable website can be exploited, and how different JavaScript payloads can be used to detect vulnerabilities. We also enumerate some tools to use for an efficient analysis. We present detailed reasoning on what can be done to improve the security of a website in order to resist attacks, and we provide supportive examples. Then, we apply an ontology model against XSS attacks to strengthen the protection of a web application. However, we note that the existence of ontology does not improve the security itself, but it has to be properly used and should require a maximum of security layers to be taken into account. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cybersecurity" title="cybersecurity">cybersecurity</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20application%20vulnerabilities" title=" web application vulnerabilities"> web application vulnerabilities</a>, <a href="https://publications.waset.org/abstracts/search?q=cyber%20threats" title=" cyber threats"> cyber threats</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology%20model" title=" ontology model"> ontology model</a> </p> <a href="https://publications.waset.org/abstracts/146344/ontology-for-cross-site-scripting-xss-attack-in-cybersecurity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146344.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">172</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">641</span> DOS and DDOS Attacks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amin%20Hamrahi">Amin Hamrahi</a>, <a href="https://publications.waset.org/abstracts/search?q=Niloofar%20Moghaddam"> Niloofar Moghaddam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Denial of Service is for denial-of-service attack, a type of attack on a network that is designed to bring the network to its knees by flooding it with useless traffic. Denial of Service (DoS) attacks have become a major threat to current computer networks. Many recent DoS attacks were launched via a large number of distributed attacking hosts in the Internet. These attacks are called distributed denial of service (DDoS) attacks. To have a better understanding on DoS attacks, this article provides an overview on existing DoS and DDoS attacks and major defense technologies in the Internet. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=denial%20of%20service" title="denial of service">denial of service</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20denial%20of%20service" title=" distributed denial of service"> distributed denial of service</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic" title=" traffic"> traffic</a>, <a href="https://publications.waset.org/abstracts/search?q=flooding" title=" flooding"> flooding</a> </p> <a href="https://publications.waset.org/abstracts/6782/dos-and-ddos-attacks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6782.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">392</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">640</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">639</span> A Survey of Domain Name System Tunneling Attacks: Detection and Prevention</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lawrence%20Williams">Lawrence Williams</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As the mechanism which converts domains to internet protocol (IP) addresses, Domain Name System (DNS) is an essential part of internet usage. It was not designed securely and can be subject to attacks. DNS attacks have become more frequent and sophisticated and the need for detecting and preventing them becomes more important for the modern network. DNS tunnelling attacks are one type of attack that are primarily used for distributed denial-of-service (DDoS) attacks and data exfiltration. Discussion of different techniques to detect and prevent DNS tunneling attacks is done. The methods, models, experiments, and data for each technique are discussed. A proposal about feasibility is made. Future research on these topics is proposed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DNS" title="DNS">DNS</a>, <a href="https://publications.waset.org/abstracts/search?q=tunneling" title=" tunneling"> tunneling</a>, <a href="https://publications.waset.org/abstracts/search?q=exfiltration" title=" exfiltration"> exfiltration</a>, <a href="https://publications.waset.org/abstracts/search?q=botnet" title=" botnet"> botnet</a> </p> <a href="https://publications.waset.org/abstracts/159239/a-survey-of-domain-name-system-tunneling-attacks-detection-and-prevention" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159239.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">75</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">638</span> Resisting Adversarial Assaults: A Model-Agnostic Autoencoder Solution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Massimo%20Miccoli">Massimo Miccoli</a>, <a href="https://publications.waset.org/abstracts/search?q=Luca%20Marangoni"> Luca Marangoni</a>, <a href="https://publications.waset.org/abstracts/search?q=Alberto%20Aniello%20Scaringi"> Alberto Aniello Scaringi</a>, <a href="https://publications.waset.org/abstracts/search?q=Alessandro%20Marceddu"> Alessandro Marceddu</a>, <a href="https://publications.waset.org/abstracts/search?q=Alessandro%20Amicone"> Alessandro Amicone</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The susceptibility of deep neural networks (DNNs) to adversarial manipulations is a recognized challenge within the computer vision domain. Adversarial examples, crafted by adding subtle yet malicious alterations to benign images, exploit this vulnerability. Various defense strategies have been proposed to safeguard DNNs against such attacks, stemming from diverse research hypotheses. Building upon prior work, our approach involves the utilization of autoencoder models. Autoencoders, a type of neural network, are trained to learn representations of training data and reconstruct inputs from these representations, typically minimizing reconstruction errors like mean squared error (MSE). Our autoencoder was trained on a dataset of benign examples; learning features specific to them. Consequently, when presented with significantly perturbed adversarial examples, the autoencoder exhibited high reconstruction errors. The architecture of the autoencoder was tailored to the dimensions of the images under evaluation. We considered various image sizes, constructing models differently for 256x256 and 512x512 images. Moreover, the choice of the computer vision model is crucial, as most adversarial attacks are designed with specific AI structures in mind. To mitigate this, we proposed a method to replace image-specific dimensions with a structure independent of both dimensions and neural network models, thereby enhancing robustness. Our multi-modal autoencoder reconstructs the spectral representation of images across the red-green-blue (RGB) color channels. To validate our approach, we conducted experiments using diverse datasets and subjected them to adversarial attacks using models such as ResNet50 and ViT_L_16 from the torch vision library. The autoencoder extracted features used in a classification model, resulting in an MSE (RGB) of 0.014, a classification accuracy of 97.33%, and a precision of 99%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adversarial%20attacks" title="adversarial attacks">adversarial attacks</a>, <a href="https://publications.waset.org/abstracts/search?q=malicious%20images%20detector" title=" malicious images detector"> malicious images detector</a>, <a href="https://publications.waset.org/abstracts/search?q=binary%20classifier" title=" binary classifier"> binary classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=multimodal%20transformer%20autoencoder" title=" multimodal transformer autoencoder"> multimodal transformer autoencoder</a> </p> <a href="https://publications.waset.org/abstracts/174687/resisting-adversarial-assaults-a-model-agnostic-autoencoder-solution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/174687.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">113</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">637</span> Static Analysis of Security Issues of the Python Packages Ecosystem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adam%20Gorine">Adam Gorine</a>, <a href="https://publications.waset.org/abstracts/search?q=Faten%20Spondon"> Faten Spondon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Python is considered the most popular programming language and offers its own ecosystem for archiving and maintaining open-source software packages. This system is called the python package index (PyPI), the repository of this programming language. Unfortunately, one-third of these software packages have vulnerabilities that allow attackers to execute code automatically when a vulnerable or malicious package is installed. This paper contributes to large-scale empirical studies investigating security issues in the python ecosystem by evaluating package vulnerabilities. These provide a series of implications that can help the security of software ecosystems by improving the process of discovering, fixing, and managing package vulnerabilities. The vulnerable dataset is generated using the NVD, the national vulnerability database, and the Snyk vulnerability dataset. In addition, we evaluated 807 vulnerability reports in the NVD and 3900 publicly known security vulnerabilities in Python Package Manager (pip) from the Snyk database from 2002 to 2022. As a result, many Python vulnerabilities appear in high severity, followed by medium severity. The most problematic areas have been improper input validation and denial of service attacks. A hybrid scanning tool that combines the three scanners bandit, snyk and dlint, which provide a clear report of the code vulnerability, is also described. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Python%20vulnerabilities" title="Python vulnerabilities">Python vulnerabilities</a>, <a href="https://publications.waset.org/abstracts/search?q=bandit" title=" bandit"> bandit</a>, <a href="https://publications.waset.org/abstracts/search?q=Snyk" title=" Snyk"> Snyk</a>, <a href="https://publications.waset.org/abstracts/search?q=Dlint" title=" Dlint"> Dlint</a>, <a href="https://publications.waset.org/abstracts/search?q=Python%20package%20index" title=" Python package index"> Python package index</a>, <a href="https://publications.waset.org/abstracts/search?q=ecosystem" title=" ecosystem"> ecosystem</a>, <a href="https://publications.waset.org/abstracts/search?q=static%20analysis" title=" static analysis"> static analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=malicious%20attacks" title=" malicious attacks"> malicious attacks</a> </p> <a href="https://publications.waset.org/abstracts/161094/static-analysis-of-security-issues-of-the-python-packages-ecosystem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161094.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">140</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">636</span> Mitigating Denial of Service Attacks in Information Centric Networking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bander%20Alzahrani">Bander Alzahrani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Information-centric networking (ICN) using architectures such as Publish-Subscribe Internet Routing Paradigm (PSIRP) is one of the promising candidates for a future Internet, has recently been under the spotlight by the research community to investigate the possibility of redesigning the current Internet architecture to solve many issues such as routing scalability, security, and quality of services issues.. The Bloom filter-based forwarding is a source-routing approach that is used in the PSIRP architecture. This mechanism is vulnerable to brute force attacks which may lead to denial-of-service (DoS) attacks. In this work, we present a new forwarding approach that keeps the advantages of Bloom filter-based forwarding while mitigates attacks on the forwarding mechanism. In practice, we introduce a special type of forwarding nodes called Edge-FW to be placed at the edge of the network. The role of these node is to add an extra security layer by validating and inspecting packets at the edge of the network against brute-force attacks and check whether the packet contains a legitimate forwarding identifier (FId) or not. We leverage Certificateless Aggregate Signature (CLAS) scheme with a small size of 64-bit which is used to sign the FId. Hence, this signature becomes bound to a specific FId. Therefore, malicious nodes that inject packets with random FIds will be easily detected and dropped at the Edge-FW node when the signature verification fails. Our preliminary security analysis suggests that with the proposed approach, the forwarding plane is able to resist attacks such as DoS with very high probability. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bloom%20filter" title="bloom filter">bloom filter</a>, <a href="https://publications.waset.org/abstracts/search?q=certificateless%20aggregate%20signature" title=" certificateless aggregate signature"> certificateless aggregate signature</a>, <a href="https://publications.waset.org/abstracts/search?q=denial-of-service" title=" denial-of-service"> denial-of-service</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20centric%20network" title=" information centric network"> information centric network</a> </p> <a href="https://publications.waset.org/abstracts/70786/mitigating-denial-of-service-attacks-in-information-centric-networking" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70786.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">198</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">635</span> The Journey of a Malicious HTTP Request </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Mansouri">M. Mansouri</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Jaklitsch"> P. Jaklitsch</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Teiniker"> E. Teiniker</a> </p> <p class="card-text"><strong>Abstract:</strong></p> SQL injection on web applications is a very popular kind of attack. There are mechanisms such as intrusion detection systems in order to detect this attack. These strategies often rely on techniques implemented at high layers of the application but do not consider the low level of system calls. The problem of only considering the high level perspective is that an attacker can circumvent the detection tools using certain techniques such as URL encoding. One technique currently used for detecting low-level attacks on privileged processes is the tracing of system calls. System calls act as a single gate to the Operating System (OS) kernel; they allow catching the critical data at an appropriate level of detail. Our basic assumption is that any type of application, be it a system service, utility program or Web application, “speaks” the language of system calls when having a conversation with the OS kernel. At this level we can see the actual attack while it is happening. We conduct an experiment in order to demonstrate the suitability of system call analysis for detecting SQL injection. We are able to detect the attack. Therefore we conclude that system calls are not only powerful in detecting low-level attacks but that they also enable us to detect high-level attacks such as SQL injection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Linux%20system%20calls" title="Linux system calls">Linux system calls</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20attack%20detection" title=" web attack detection"> web attack detection</a>, <a href="https://publications.waset.org/abstracts/search?q=interception" title=" interception"> interception</a>, <a href="https://publications.waset.org/abstracts/search?q=SQL" title=" SQL "> SQL </a> </p> <a href="https://publications.waset.org/abstracts/13242/the-journey-of-a-malicious-http-request" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13242.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">359</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">634</span> A Review of Ultralightweight Mutual Authentication Protocols</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Umar%20Mujahid">Umar Mujahid</a>, <a href="https://publications.waset.org/abstracts/search?q=Greatzel%20Unabia"> Greatzel Unabia</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongsik%20Choi"> Hongsik Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Binh%20Tran"> Binh Tran</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Radio Frequency Identification (RFID) is one of the most commonly used technologies in IoTs and Wireless Sensor Networks which makes the devices identification and tracking extremely easy to manage. Since RFID uses wireless channel for communication, which is open for all types of adversaries, researchers have proposed many Ultralightweight Mutual Authentication Protocols (UMAPs) to ensure security and privacy in a cost-effective manner. These UMAPs involve simple bitwise logical operators such as XOR, AND, OR & Rot, etc., to design the protocol messages. However, most of these UMAPs were later reported to be vulnerable against many malicious attacks. In this paper, we have presented a detailed overview of some eminent UMAPs and also discussed the many security attacks on them. Finally, some recommendations and suggestions have been discussed, which can improve the design of the UMAPs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=RFID" title="RFID">RFID</a>, <a href="https://publications.waset.org/abstracts/search?q=Ultralightweight" title=" Ultralightweight"> Ultralightweight</a>, <a href="https://publications.waset.org/abstracts/search?q=UMAP" title=" UMAP"> UMAP</a>, <a href="https://publications.waset.org/abstracts/search?q=SASI" title=" SASI"> SASI</a> </p> <a href="https://publications.waset.org/abstracts/119955/a-review-of-ultralightweight-mutual-authentication-protocols" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/119955.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">153</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">633</span> Comprehensive Review of Adversarial Machine Learning in PDF Malware</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Preston%20Nabors">Preston Nabors</a>, <a href="https://publications.waset.org/abstracts/search?q=Nasseh%20Tabrizi"> Nasseh Tabrizi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Portable Document Format (PDF) files have gained significant popularity for sharing and distributing documents due to their universal compatibility. However, the widespread use of PDF files has made them attractive targets for cybercriminals, who exploit vulnerabilities to deliver malware and compromise the security of end-user systems. This paper reviews notable contributions in PDF malware detection, including static, dynamic, signature-based, and hybrid analysis. It presents a comprehensive examination of PDF malware detection techniques, focusing on the emerging threat of adversarial sampling and the need for robust defense mechanisms. The paper highlights the vulnerability of machine learning classifiers to evasion attacks. It explores adversarial sampling techniques in PDF malware detection to produce mimicry and reverse mimicry evasion attacks, which aim to bypass detection systems. Improvements for future research are identified, including accessible methods, applying adversarial sampling techniques to malicious payloads, evaluating other models, evaluating the importance of features to malware, implementing adversarial defense techniques, and conducting comprehensive examination across various scenarios. By addressing these opportunities, researchers can enhance PDF malware detection and develop more resilient defense mechanisms against adversarial attacks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adversarial%20attacks" title="adversarial attacks">adversarial attacks</a>, <a href="https://publications.waset.org/abstracts/search?q=adversarial%20defense" title=" adversarial defense"> adversarial defense</a>, <a href="https://publications.waset.org/abstracts/search?q=adversarial%20machine%20learning" title=" adversarial machine learning"> adversarial machine learning</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=PDF%20malware" title=" PDF malware"> PDF malware</a>, <a href="https://publications.waset.org/abstracts/search?q=malware%20detection" title=" malware detection"> malware detection</a>, <a href="https://publications.waset.org/abstracts/search?q=malware%20detection%20evasion" title=" malware detection evasion"> malware detection evasion</a> </p> <a href="https://publications.waset.org/abstracts/184556/comprehensive-review-of-adversarial-machine-learning-in-pdf-malware" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184556.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">39</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">632</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/abstracts/search?q=Najmeh%20Abedzadeh">Najmeh Abedzadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Matthew%20Jacobs"> Matthew Jacobs</a> </p> <p class="card-text"><strong>Abstract:</strong></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 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=imbalanced%20datasets" title=" imbalanced datasets"> imbalanced datasets</a>, <a href="https://publications.waset.org/abstracts/search?q=sampling%20algorithms" title=" sampling algorithms"> sampling algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a> </p> <a href="https://publications.waset.org/abstracts/149498/a-survey-in-techniques-for-imbalanced-intrusion-detection-system-datasets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149498.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 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