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Real-Time Inference of Topological Structure and Vulnerabilities for Adaptive Jamming Against Covert Ad Hoc Networks
Computer Science and Engineering
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With the emerging reliance of critical communications on ad hoc architectures, ensuring the security of such networks is paramount. Even though the independence of ad hoc networks from a single point of failure is seen as an advantage, the distributed nature of ad hoc communications introduces a variety of complex security problems. These problems are further intensified in mission critical networks deployed in hostile environments such as modern battlefields, where analysis and disruption of opponents' wireless communications is an essential component of combat. Therefore, resilience of network connectivity to disruption and concealment of communications is a priority in design of critical ad hoc networks. To this end, various techniques have been proposed for mitigation of disruptive attacks, the majority of which focus on routing and upper layers of the protocol stack, while very few consider implementing mitigation in the physical and link layers.This thesis aims at demonstrating the vulnerability of covert ad hoc networks to adaptive jamming attacks that rely only on physical layer parameters. A novel transmission timing analysis technique is proposed to estimate the existence of hop-to-hop links based on the synchronicity of transmission timings in both time and frequency domains, complemented with a minimal thresholding method for classification of link estimations. Furthermore, this work proposes a computationally efficient method for identification of the most vulnerable region of the network via graph theoretical modeling. The computational cost of this method is further reduced by employment of a fast search space generation algorithm, as well as percolation modeling of the system. Both methods are shown to increase the efficiency of adaptive jamming when no a priori information about the topology or protocols of the network is available. Performance of the proposed methods is measured through graph theoretical and network simulations.