IEEE Access | |
DESOLATER: Deep Reinforcement Learning-Based Resource Allocation and Moving Target Defense Deployment Framework | |
Hyuk Lim1  Jin-Hee Cho2  Seunghyun Yoon3  Dong Seong Kim4  Terrence J. Moore5  Frederica Free-Nelson5  | |
[1] AI Graduate School, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea;Department of Computer Science, Virginia Tech, Falls Church, VA, USA;School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea;School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, QLD, Australia;U.S. Army Research Laboratory, Adelphi, MD, USA; | |
关键词: Deep reinforcement learning; in-vehicle network; moving target defense; network slicing; partial observability; software-defined networking; | |
DOI : 10.1109/ACCESS.2021.3076599 | |
来源: DOAJ |
【 摘 要 】
The recent development of autonomous driving technologies has led to the proliferation of research on sensors and electronic equipment inside a vehicle. To deal with security concerns of in-vehicle networks, various deep learning (DL) and reinforcement learning (RL) have been developed to enhance in-vehicle security. However, the DL/RL agents are vulnerable to adversarial perturbation, where an attacker can perform a manipulation attack to interfere with the agent’s operation. In this work, we aim to develop two key mechanisms to build secure in-vehicle networks: (1) RL-based proactive defense mechanism to achieve multiple objectives of minimizing system security vulnerabilities while maximizing service availability; and (2) a resilient RL method that allows an agent to operate in the presence of adversarial disturbances that neutralize the system security. To this end, we propose, DESOLATER (
【 授权许可】
Unknown