Efficient Adversarial Attacks Against DRL-Based Resource Allocation in Intelligent O-RAN for V2X

• Published in: IEEE transactions on vehicular technology Vol. 74; no. 1; pp. 1674 - 1686
• Main Authors: Ergu, Yared Abera, Nguyen, Van-Linh, Hwang, Ren-Hung, Lin, Ying-Dar, Cho, Chuan-Yu, Yang, Hui-Kuo, Shin, Hyundong, Duong, Trung Q.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adversarial attacks; Artificial intelligence; deep reinforcement learning; Glass box; Infiltration; Intelligent vehicles; Jamming; Machine learning; Network latency; Network security; O-RAN; Open RAN; Perturbation methods; policy infiltration attacks; Real time; Resource allocation; Resource management; Roads; Security; Vehicle-to-everything; Wireless communication
• DOI: 10.1109/TVT.2024.3466511
• ISSN: 0018-9545

Artificial intelligence (AI) is projected to be a critical part of open radio access networks (O-RAN) to enable intelligence for connectivity management in smart vehicle-to-everything (V2X) networks and vehicle road cooperation systems. However, the openness and dependence of AI models on massive volumes of data render them subject to serious security vulnerabilities, such as adversarial attacks. This study investigates security issues in O-RAN's near real-time RAN intelligent controller (RIC), with an emphasis on deep reinforcement learning (DRL)-based resource allocation. We introduce a novel attack manipulating environmental observations to mislead AI agents, resulting in erroneous allocations and decreased physical resource block (PRB) transmission rates for various vehicular communications. We also discover flaws where compromised users or signal jammers can fake signal power to trick the AI agent's state observation. This can lead to a policy infiltration attack that makes the network performance drop significantly. Evaluation results show up to a 40% decline in user data rates, a 77.74% reduction in packet delivery rates, and significant disruptions in ultra-reliable and low-latency communications (uRLLC) services such as remote driving and connected automated vehicles. The policy infiltration attack causes a 20% increase in packet losses and up to 150% delay overall. The attack efficiency emphasizes the need for adversarial training in protecting AI-driven applications, which should be addressed in future O-RAN security specifications and AI-powered vehicular networks.