TY - JOUR
T1 - Resilient dynamic channel access via robust deep reinforcement learning
AU - Wang, Feng
AU - Zhong, Chen
AU - Gursoy, M. Cenk
AU - Velipasalar, Senem
N1 - Publisher Copyright:
© 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
PY - 2021
Y1 - 2021
N2 - 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 DRL-based 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.
AB - 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 DRL-based 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.
KW - Adversarial policies
KW - Deep reinforcement learning
KW - Defense strategies
KW - Dynamic channel access
KW - Jamming attacks
UR - http://www.scopus.com/inward/record.url?scp=85121372148&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85121372148&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2021.3133506
DO - 10.1109/ACCESS.2021.3133506
M3 - Article
AN - SCOPUS:85121372148
SN - 2169-3536
VL - 9
SP - 163188
EP - 163203
JO - IEEE Access
JF - IEEE Access
ER -