Provably Secure Competitive Routing against Proactive Byzantine Adversaries via Reinforcement Learning

An ad hoc wireless network is an autonomous selforganizing system of mobile nodes connected by wireless links where nodes not in direct range communicate via intermediary nodes. Routing in ad hoc networks is a challenging problem as a result of highly dynamic topology as well as bandwidth and energy constraints. The Swarm Intelligence paradigm has recently been demonstrated as an effective approach for routing in small static network configurations with no adversarial intervention. These algorithms have also been proven to be robust and resilient to changes in node configuration. However, none of the existing routing algorithms can withstand a dynamic proactive adversarial attack, where the network may be completely controlled by byzantine adversaries. The routing protocol presented in this work attempts to provide throughput competitive route selection against an adversary which is essentially unlimited; more specifically, the adversary benefits from complete collusion of adversarial nodes, can engage in arbitrary byzantine behavior and can mount arbitrary selective adaptive attacks, dynamically changing its attack with each new packet. In this work, we show how to use the Swarm Intelligence paradigm and Distributed Reinforcement Learning in order to develop provably secure routing against byzantine adversaries. A proof of the convergence time of our algorithm is presented as well as preliminary simulation results.