NASA’s new dark energy space telescope can also detect killer asteroids
technologyreview.com·3h ago
AI Research
arXiv CS.AI·May 21·5 min read
When Critics Disagree: Adaptive Reward Poisoning Attacks in RIS-Aided Wireless Control System
TL;DR
Learning-based wireless control systems are vulnerable to reward-poisoning attacks, which can compromise their performance. An adaptive attack method called Disagreement-Guided Reward Poisoning (DGRP) was developed for a Soft Actor-Critic (SAC) agent in a Cognitive Radio Network (CRN) with Reconfigurable Intelligent Surfaces (RIS).
✦ Why It Matters
Engineers should consider adaptive attack strategies when designing defenses for learning-based wireless control systems.
Key Takeaways
- Implement robust defenses against reward-poisoning attacks.
- Explore adaptive strategies for learning-based systems.
- Evaluate the impact of adversarial interference on performance.
- Consider the role of Reconfigurable Intelligent Surfaces in wireless networks.
Full Summary
Wireless control systems that rely on learning algorithms face significant threats from reward-poisoning attacks, which can distort the learning process by manipulating the reward signals. To address this vulnerability, a novel attack strategy named Disagreement-Guided Reward Poisoning (DGRP) was introduced, targeting a Soft Actor-Critic (SAC) agent operating within a Cognitive Radio Network (CRN) enhanced by Reconfigurable Intelligent Surfaces (RIS).
The SAC agent's goal is to optimize the transmission power to maximize the long-term rate for secondary users (SUs). The DGRP method adapts its attack based on the disagreement among agents, making it more effective against traditional defenses.
Experimental results demonstrated that DGRP significantly reduced the performance of the SAC agent, highlighting the need for robust defenses in such systems. These findings underscore the importance of developing adaptive strategies to safeguard learning-based control systems from sophisticated attacks.