Thesis (Ph.D.)--University of Wyoming, 2008. / Title from PDF title page (viewed on August 3, 2009). Includes bibliographical references (p. 73-84).

Thesis (M.S.)--University of Texas at Dallas, 2008. / Includes vita. Includes bibliographical references (leaves 39-40)

Thesis (M.S.)--Oregon State University, 2009. / Printout. Includes bibliographical references (leaves 45-46). Also available on the World Wide Web.

Suen, Ho Yan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 99-104). / Abstract also in Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Organization --- p.4 / Chapter 2 --- Literature Review --- p.6 / Chapter 2.1 --- Related Works --- p.6 / Chapter 2.2 --- Background Study --- p.7 / Chapter 2.2.1 --- World Wide Web --- p.7 / Chapter 2.2.2 --- Distributed Denial of Service Attack --- p.11 / Chapter 2.2.3 --- Tools for Dimension Reduction --- p.13 / Chapter 2.2.4 --- Tools for Anomaly Detection --- p.20 / Chapter 2.2.5 --- Receiver operating characteristics (ROC) Analysis --- p.22 / Chapter 3 --- System Design --- p.25 / Chapter 3.1 --- Methodology --- p.25 / Chapter 3.2 --- System Overview --- p.27 / Chapter 3.3 --- Reference Profile Construction --- p.31 / Chapter 3.4 --- Real-time Anomaly Detection and Response --- p.32 / Chapter 3.5 --- Chapter Summary --- p.34 / Chapter 4 --- Reference Profile Construction --- p.35 / Chapter 4.1 --- Web Access Logs Collection --- p.35 / Chapter 4.2 --- Data Preparation --- p.37 / Chapter 4.3 --- Feature Extraction and Embedding Engine (FEE Engine) --- p.40 / Chapter 4.3.1 --- Sub-Sequence Extraction --- p.42 / Chapter 4.3.2 --- Hash Function on Sub-sequences (optional) --- p.45 / Chapter 4.3.3 --- Feature Vector Construction --- p.46 / Chapter 4.3.4 --- Diffusion Wavelets Embedding --- p.47 / Chapter 4.3.5 --- Numerical Example of Feature Set Reduction --- p.49 / Chapter 4.3.6 --- Reference Profile and Further Use of FEE Engine --- p.50 / Chapter 4.4 --- Chapter Summary --- p.50 / Chapter 5 --- Real-time Anomaly Detection and Response --- p.52 / Chapter 5.1 --- Session Filtering and Data Preparation --- p.54 / Chapter 5.2 --- Feature Extraction and Embedding --- p.54 / Chapter 5.3 --- Distance-based Outlier Scores Calculation --- p.55 / Chapter 5.4 --- Anomaly Detection and Response --- p.56 / Chapter 5.4.1 --- Length-Based Anomaly Detection Modules --- p.56 / Chapter 5.4.2 --- Characteristics of Anomaly Detection Modules --- p.59 / Chapter 5.4.3 --- Dynamic Threshold Adaptation --- p.60 / Chapter 5.5 --- Chapter Summary --- p.63 / Chapter 6 --- Experimental Results --- p.65 / Chapter 6.1 --- Experiment Datasets --- p.65 / Chapter 6.1.1 --- Normal Web Access Logs --- p.66 / Chapter 6.1.2 --- Attack Data Generation --- p.68 / Chapter 6.2 --- ROC Curve Construction --- p.70 / Chapter 6.3 --- System Parameters Selection --- p.71 / Chapter 6.4 --- Performance of Anomaly Detection --- p.82 / Chapter 6.4.1 --- Performance Analysis --- p.85 / Chapter 6.4.2 --- Performance in defending DDoS attacks --- p.87 / Chapter 6.5 --- Computation Requirement --- p.91 / Chapter 6.6 --- Chapter Summary --- p.95 / Chapter 7 --- Conclusion and Future Work --- p.96 / Bibliography --- p.99

Thesis (M.S.)--Oregon State University, 2008. / Printout. Includes bibliographical references (leaves 53-57). Also available on the World Wide Web.

Thesis (Ph. D.)--University of Alabama at Birmingham, 2007. / Additional advisors: Elliot J. Lefkowitz, Kevin D. Reilly, Robert Thacker, Chengcui Zhang. Description based on contents viewed Feb. 7, 2008; title from title screen. Includes bibliographical references (p. 125-132).

Dissertation (Ph.D.)--Auburn University, 2006. / Abstract. Vita. Includes bibliographic references (p.149-156).

<p dir="ltr">The rapid advancement of autonomous vehicles (AVs) introduces complex cybersecurity challenges within Vehicular Ad-hoc Networks (VANETs). Despite the adoption of Artificial Intelligence (AI) for anomaly detection, a critical gap remains in both the explainability of AI models and the robustness of VANET frameworks against cyber intrusions, which limits trust, transparency, and resilience. This thesis addresses these gaps by proposing a multi-faceted, end-to-end explainable AI (XAI) framework alongside innovative security mechanisms to safeguard AV networks from potential attackers. In the initial chapter, we present an XAI framework that applies novel feature selection methods based on Shapley Additive Explanations (SHAP) to improve transparency in anomaly detection for AVs. The framework integrates global and local XAI approaches, offering interpretability across six black-box models and demonstrating superior performance over state-of-the-art feature selection techniques. The framework’s efficacy is validated through application to two AV datasets, showcasing improvements in both efficiency and generalizability. The second chapter builds upon this by systematically evaluating the effectiveness of XAI methods—namely SHAP and Local Interpretable Model-agnostic Explanations (LIME)— across multiple metrics. Through a rigorous benchmarking process on two autonomous driving datasets, this chapter highlights the strengths and limitations of each XAI technique, offering a foundational framework for transparency in AV cybersecurity and encouraging further research through publicly available resources. In the third chapter, we explore a security framework for platoon-based AV networks, addressing the need for secure and efficient highway usage. This framework introduces a two-phase anomaly detection system, incorporating an authenticity scoring mechanism and an LSTM-based roadside unit (RSU) for network-wide monitoring. Enhanced by group-based signatures and dynamic channel-switching, this approach defends against man-in-the-middle (MITM) and denial-of-service (DoS) attacks, demonstrating resilience through extensive simulation results. The final chapter examines the security of decentralized, Directed Acyclic Graph (DAG) based AV networks, which, while promising for scalability, are susceptible to unique cyber threats. We propose and evaluate four targeted attack scenarios alongside corresponding defense strategies across five DAG structures. This analysis reveals the resilience of different DAG configurations under attack, advancing the understanding of structural cybersecurity for decentralized AV networks. In summary, this thesis develops comprehensive frameworks and methodologies to enhance the security and interpretability of AV networks, bridging critical gaps in XAI and cybersecurity for anomaly detection and intrusion defense in AV environments.</p>

The integrity of network communications is constantly being challenged by more sophisticated intrusion techniques. Attackers are shifting to stealthier and more complex forms of attacks in an attempt to bypass known mitigation strategies. Also, many detection methods for popular network attacks have been developed using outdated or non-representative attack data. To effectively develop modern detection methodologies, there exists a need to acquire data that can fully encompass the behaviors of persistent and emerging threats. When collecting modern day network traffic for intrusion detection, substantial amounts of traffic can be collected, much of which consists of relatively few attack instances as compared to normal traffic. This skewed distribution between normal and attack data can lead to high levels of class imbalance. Machine learning techniques can be used to aid in attack detection, but large levels of imbalance between normal (majority) and attack (minority) instances can lead to inaccurate detection results. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Electrical Engineering, 2008. / Includes bibliographical references.