Evolving defensive strategies against iterated induction attacks in cognitive radio networks

This paper investigates the use of Genetic Algorithms (GAs) to evolve defensive strategies against iterated and memory enabled induction attacks in cognitive radio networks. Security problems in cognitive radio networks have been heavily studied in recent years. However, few studies have considered the effect of memory size on attack and defense strategies. We model cognitive radio network attack and defense as a zero-sum stochastic game. Our research focuses on using GAs to recognize attack patterns from different attackers and evolving defensive strategies against the attack patterns so as to maximize network utility. We assume attackers are not only able to attack high utility channels, but are also capable of attacking based on the history of high utility channel usage by the secondary user. In our simulations, different memory lengths are used by the secondary user against memory enabled attackers. Results show that the best performance strategies evolved by GAs gain more payoff, on average, than the Nash equilibrium. Against our baseline memory enabled attackers, GAs quickly and reliably found the theoretically globally optimal defensive strategy. These results indicate that GAs is a viable approach for generating strong defenses against arbitrary memory based attackers.