Adversarial Jamming Attacks on Deep Reinforcement Learning Based Dynamic Multichannel Access
Chen Zhong, Feng Wang, M. Cenk Gursoy, Senem Velipasalar
Abstract
Adversarial attack strategies have been widely studied in machine learning applications, and now are increasingly attracting interest in wireless communications as the application of machine learning methods to wireless systems grows along with security concerns. In this paper, we propose two adversarial policies, one based on feed-forward neural networks (FNNs) and the other based on deep reinforcement learning (DRL) policies. Both attack strategies aim at minimizing the accuracy of a DRL-based dynamic channel access agent. We first present the two frameworks and the dynamic attack procedures of the two adversarial policies. Then we demonstrate and compare their performances. Finally, the advantages and disadvantages of the two frameworks are identified.