Artificial Intelligence-based Attack and Countermeasure Agents: Who wins? An Invited Paper

Abstract

Reinforcement learning (RL) is a branch of artificial intelligence that has been well investigated to show system performance enhancement. Yet, the investigation into the security aspects of RL is at its infancy. This paper offers an investigation of the security aspect of RL. In this paper, RL is applied to cluster size adjustment in clustering for distributed cognitive radio networks whereby the unlicensed or secondary users (SUs) explore and use underutilized channels (or white spaces) owned by the licensed or primary users. Clustering segregates SUs in a network into logical groups; and each cluster consists of a leader (or a clusterhead) and member nodes. The investigation covers the effects of an important RL parameter, specifically the learning rate α, in a dynamic operating environment. Both clusterhead and member node leverage on RL: a) the clusterhead uses RL to countermeasure attacks, and b) the SU uses RL to launch attacks with various attack probabilities. Simulation results have shown that a RL model with learning rate α = 1 for clusterhead provides the highest network scalability when being attacked with various attack probabilities and different learning rates in a dynamic operating environment.