Black-box attacks raise error rates in wireless IRS communications
Black-Box Attack for IRS-Aided Communications with BER-Only Feedback
Information TheorySocial and Information Networks
Summary
Intelligent reflecting surfaces (IRS) improve wireless signals by bouncing them cleverly. This paper looks at a sneaky attack where a bad actor changes the IRS settings without knowing the signal details, only seeing error rates reported by users. The attackers try different IRS setups to make communication worse for everyone, but they can't try endlessly. The authors develop a clever search method called PRIS that learns from past tries and zooms in on the worst IRS setups, even though the feedback is noisy. Tests show PRIS makes communication errors worse than previous attack methods.
What this means in practice
- •For wireless security teams: Evaluate potential IRS vulnerability by simulating black-box attacks with limited signal feedback to improve defense strategies.
- •For network operators: Test IRS configurations’ resilience to unknown configuration attacks using BER-only feedback to enhance network reliability.
Authors
Zhengkai Tu, Jimmy Huang
Abstract
Intelligent reflecting surface (IRS) has emerged as a promising wireless technology for enhancing legitimate communications. In this paper, we investigate an IRS-assisted multiuser MISO downlink network in which an attacker reconfigures the IRS to degrade legitimate data transmission. Specifically, we consider a black-box attack setting in which the attacker has access to neither channel information nor symbol-level observations and can observe only the BER reported by each user. The objective of the attacker is to identify an IRS configuration that maximizes the minimum BER among all users under a limited budget of non-repeated tests. To address this BER-only black-box attack problem, we propose PRIS, an online search method that learns effective phase transitions from previous trials and progressively narrows the search from large-block modifications to single-element refinement. Specifically, an annealed user-balancing reward and prioritized replay are incorporated to exploit noisy BER feedback. Numerical results demonstrate that PRIS achieves a higher minimum BER than existing benchmarks.