3 research outputs found
Adversarial jamming attacks and defense strategies via adaptive deep reinforcement learning
As the applications of deep reinforcement learning (DRL) in wireless
communications grow, sensitivity of DRL based wireless communication strategies
against adversarial attacks has started to draw increasing attention. In order
to address such sensitivity and alleviate the resulting security concerns, we
in this paper consider a victim user that performs DRL-based dynamic channel
access, and an attacker that executes DRLbased jamming attacks to disrupt the
victim. Hence, both the victim and attacker are DRL agents and can interact
with each other, retrain their models, and adapt to opponents' policies. In
this setting, we initially develop an adversarial jamming attack policy that
aims at minimizing the accuracy of victim's decision making on dynamic channel
access. Subsequently, we devise defense strategies against such an attacker,
and propose three defense strategies, namely diversified defense with
proportional-integral-derivative (PID) control, diversified defense with an
imitation attacker, and defense via orthogonal policies. We design these
strategies to maximize the attacked victim's accuracy and evaluate their
performances.Comment: 13 pages, 24 figure