Backdoor Attack Against Deep Reinforcement Learning-based Spectrum Access Model

Abstract

Deep reinforcement learning(DRL) has attracted much attention in multi-user intelligent dynamic spectrum access due to its advantages in sensing and decision making.However,the weak interpretability of deep neural networks(DNNs) makes DRL models vulnerable to backdoor attacks.In this paper,a non-invasive backdoor attack method with low-cost is proposed against DSA-oriented DRL models in cognitive wireless networks.The attacker monitors the wireless channels to select backdoor triggers,and generates backdoor samples into the experience pool of a secondary user's DRL model.Then,the trigger can be implanted into the DRL model during the training phase.The attacker actively sends signals to activate the triggers in the DRL model during the inference phase,inducing secondary users to take the actions set by the attacker,thereby reducing their success rate of channel access.A series of simulation show that the proposed backdoor attack method can reduce the attack cost by 20%~30% while achieving an attack success rate over 90%,and is suitable for three different DRL models.

Keywords

• dynamic spectrum access|deep reinforcement learning|backdoor attack|trigger