In this paper, we first address adverse effects of cyber-physical attacks on distributed synchronization of multiagent systems, by providing conditions under which an attacker can destabilize the underlying network, as well as another set of conditions under which local neighborhood tracking errors of intact agents converge to zero. Based on this analysis, we propose a Kullback-Liebler divergence based criterion in view of which each agent detects its neighbors' misbehavior and, consequently, forms a self-belief about the trustworthiness of the information it receives. Agents continuously update their selfbeliefs and communicate them with their neighbors to inform them of the significance of their outgoing information. Moreover, if the self-belief of an agent is low, it forms trust on its neighbors. Agents incorporate their neighbors' self-beliefs and their own trust values on their control protocols to slow down and mitigate attacks. We show that using the proposed resilient approach, an agent discards the information it receives from a neighbor only if its neighbor is compromised, and not solely based on the discrepancy among neighbors' information, which might be caused by legitimate changes, and not attacks. The proposed approach is guaranteed to work under mild connectivity assumptions.
This work presents a rigorous analysis of the adverse effects of cyber-physical attacks on discrete-time distributed multi-agent systems, and propose a mitigation approach for attacks on sensors and actuators. First, we show how an attack on a compromised agent can propagate and affect intact agents that are reachable from it. That is, an attack on a single node snowballs into a network-wide attack and can even destabilize the entire system. Moreover, we show that the attacker can bypass the robust H ∞ control protocol and make it entirely ineffective in attenuating the effect of the adversarial input on the system performance. Finally, to overcome adversarial effects of attacks on sensors and actuators, a distributed adaptive attack compensator is designed by estimating the normal expected behavior of agents. The adaptive attack compensator is augmented with the controller and it is shown that the proposed controller achieves secure consensus in presence of the attacks on sensors and actuators. This controller does not require to make any restrictive assumption on the number of agents or agent's neighbors under direct effect of adversarial input. Moreover, it recovers compromised agents under actuator attacks and avoids propagation of attacks on sensors without removing compromised agents. The effectiveness of the proposed controller and analysis is validated on a network of Sentry autonomous underwater vehicles subject to attacks under different scenarios.
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