Every environment is susceptible to risks and Wireless Local Area Networks (WLANs) based on the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard are no exception. The most apparent risk of WLANs is the ease with which itinerant intruders obtain illicit entry into these networks. These intrusion security risks must therefore be addressed which means that information security risk analysis and risk management need to be considered as integral elements of the organisation’s business plan. A well-established qualitative risk analysis and risk management methodology, the Operationally Critical Threat Asset and Vulnerability Evaluation (OCTAVE) is selected for conducting the WLAN intrusion security risk analysis and risk management process. However, the OCTAVE risk analysis methodology is beset with a number of problems that could hamper a successful WLAN intrusion security risk analysis. The ultimate deliverable of this qualitative risk analysis methodology is the creation of an organisation-wide protection strategy and risk mitigation plan. Achieving this end using the OCTAVE risk analysis methodology requires an inordinate amount of time, ranging from months to years. Since WLANs are persistently under attack, there is a dire need for an expeditious risk analysis methodology. Furthermore, the OCTAVE risk analysis methodology stipulates the identification of assets and corresponding threat scenarios via a brainstorming session, which may be beyond the scope of a person who is not proficient in information security issues. This research was therefore inspired by the pivotal need for a risk analysis and risk management methodology to address WLAN intrusion attacks and the resulting risks they pose to the confidentiality, integrity and availability of information processed by these networks. Copyright / Dissertation (MSc (Computer Science))--University of Pretoria, 2006. / Computer Science / unrestricted

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This research investigates wireless intrusion detection techniques for detecting attacks on IEEE 802.11i Robust Secure Networks (RSNs). Despite using a variety of comprehensive preventative security measures, the RSNs remain vulnerable to a number of attacks. Failure of preventative measures to address all RSN vulnerabilities dictates the need for a comprehensive monitoring capability to detect all attacks on RSNs and also to proactively address potential security vulnerabilities by detecting security policy violations in the WLAN. This research proposes novel wireless intrusion detection techniques to address these monitoring requirements and also studies correlation of the generated alarms across wireless intrusion detection system (WIDS) sensors and the detection techniques themselves for greater reliability and robustness. The specific outcomes of this research are: A comprehensive review of the outstanding vulnerabilities and attacks in IEEE 802.11i RSNs. A comprehensive review of the wireless intrusion detection techniques currently available for detecting attacks on RSNs. Identification of the drawbacks and limitations of the currently available wireless intrusion detection techniques in detecting attacks on RSNs. Development of three novel wireless intrusion detection techniques for detecting RSN attacks and security policy violations in RSNs. Development of algorithms for each novel intrusion detection technique to correlate alarms across distributed sensors of a WIDS. Development of an algorithm for automatic attack scenario detection using cross detection technique correlation. Development of an algorithm to automatically assign priority to the detected attack scenario using cross detection technique correlation.

<p dir="ltr">The research work presented in this thesis focuses on two highly important topics in the modern age. The first topic of research is the development of various image-based Network Intrusion Detection Systems (NIDSs) and performing a comprehensive analysis of their performance. Wi-Fi networks have become ubiquitous in enterprise and home networks which creates opportunities for attackers to target the networks. These attackers exploit various vulnerabilities in Wi-Fi networks to gain unauthorized access to a network or extract data from end users' devices. The deployment of an NIDS helps detect these attacks before they can cause any significant damages to the network's functionalities or security. Within the scope of our research, we provide a comparative analysis of various deep learning (DL)-based NIDSs that utilize various imaging techniques to detect anomalous traffic in a Wi-Fi network. The second topic in this thesis is the development of learning modules for security and privacy in Machine Learning (ML). The increasing integration of ML in various domains raises concerns about its security and privacy. In order to effectively address such concerns, students learning about the basics of ML need to be made aware of the steps that are taken to develop robust and secure ML-based systems. As part of this, we introduce a set of hands-on learning modules designed to educate students on the importance of security and privacy in ML. The modules provide a theoretical learning experience through presentations and practical experience using Python Notebooks. The modules are developed in a manner that allows students to easily absorb the concepts regarding privacy and security of ML models and implement it in real-life scenarios. The efficacy of this process will be obtained from the results of the surveys conducted before and after providing the learning modules. Positive results from the survey will demonstrate the learning modules were effective in imparting knowledge to the students and the need to incorporate security and privacy concepts in introductory ML courses.</p>