Overview
(1) 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 project 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.
(2) The governing dynamics of many multi-agent systems and distributed networks, such as IoT and social interactions, can be captured within the framework of network formation games. Adopting the tools of this framework, we developed a multi-agent model for IoT nodes that aim to maximize their individual performances, and studied the effect of such agent-centric behavior on the formation dynamics of various topologies. We present a formal treatment of this problem, and establish analytical bounds on the parameters of payoff functions and network configuration for emergence of stable cliques, as well as star and hierarchical topologies.
Current Team Members:
Affiliate Research Groups:
Computer Networking Lab (University of Nevada, Reno)
Tools and Datasets:
Publications:
AI Safety & Security