The significant increase in the number of interconnected devices has brought new services and applications, as well as new network vulnerabilities. The increasing hardware capacities of these devices and the developments in the artificial intelligence field mean that new and complex attack methods are being developed. This chapter focuses on the backoff attack in a wireless network using CSMA/CA multiple access, and it shows that an intelligent attacker, making use of control theory, can successfully exploit a sequential probability ratio test-based defense mechanism. Also, recent developments in the deep reinforcement learning field allows that attackers that do not have full knowledge of the defense mechanism are able to successfully learn to attack it. Thus, this chapter illustrates by means of the backoff attack, the possibilities that the recent advances in the artificial intelligence field bring to intelligent attackers, and highlights the importance of researching in intelligent defense methods able to cope with such attackers.
Deep Reinforcement Learning for Attacking Wireless Sensor Networks