Date of Award

Spring 5-24-2025

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Cyberspace Engineering

First Advisor

Pradeep Chowriappa

Abstract

Wireless networks, susceptible to a range of attacks due to their simplicity and ease of evasion, face a significant threat from control data attacks, notably the elusive wormhole attack. Detecting and mitigating such attacks poses challenges, particularly in the absence of a digital signature. This dissertation introduces an innovative approach that utilizes the propagation delay associated with malicious nodes’ timing characteristics for detection, employing the Ad-hoc On-Demand Distance Vector (AODV) algorithm as its foundation. The inherent propagation delay in the AODV protocol is calculated for each node link along the entire communication path, offering a distinctive timing method that provides comprehensive information for all links. The detection process requires transmitting information to the destination node, which employs box plot statistical analysis to detect outliers. Malicious nodes are then tagged with a weighting factor, and the mitigation procedure involves dynamically adjusting these factors until a boundary condition is triggered. Once flagged as malicious, the link is eliminated from the preferred path, and an alternative path is selected, ensuring a self-leveling mechanism for ongoing correction. This research creates an efficient method for identifying and mitigating the out-of-band channel wormhole attack, using propagation delay with minimal overhead. The presented method offers a streamlined and effective approach to enhance the security of wireless networks against this sophisticated form of attack, hinting at potential enhancements through the exploration of digital signatures obtained from box plot information. Unlike traditional methods that rely on signature-based detection or specialized hardware, our method focuses on analyzing propagation delay timings to identify anomalous behavior indicative of wormhole attacks. This methodology involves collecting propagation delay data in both normal network scenarios and scenarios with inserted malicious wormhole nodes. By comparing these delay timings, our approach differentiates legitimate network paths and potential wormhole shortcuts. Utilizing the NS-3 network simulator, we validate the effectiveness of our method in accurately detecting and mitigating wormhole attacks. The key advantage of our approach lies in its proactive nature and ability to detect wormholes without relying on specific attack signatures or additional hardware. Using the consistency of propagation delay data, the AODV-PD method offers a promising avenue for early detection and prevention of wormhole attacks, thereby enhancing network security and reliability. The results and insights presented in this dissertation contribute to the ongoing efforts in developing defense mechanisms against sophisticated network attacks, emphasizing the potential of propagation delay analysis in addressing the challenges posed by wormhole threats in wireless networks.

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