Secure Distributed RIS-MIMO over Double Scattering Channels: Adversarial Attack, Defense, and SER Improvement

2025-11-02

AI Summary

This work addresses adversarial security in distributed reconfigurable intelligent surface (RIS)-assisted MIMO communications under limited scattering. We propose an autoencoder-based end-to-end robust framework. For the first time, we model multi-RIS cooperative scenarios under double-scattering channels, revealing the dual effect of increasing RIS count: improved symbol error rate (SER) under benign conditions but heightened adversarial vulnerability. Methodologically, we integrate statistical channel modeling, adversarial training, and Doppler compensation to ensure robust decoding under mobility. Experiments demonstrate that our defense significantly reduces SER under attacks while outperforming baseline methods even in attack-free settings, and maintains stability under high-speed mobility. Our core contribution is establishing the first adversarial security analysis paradigm for distributed multi-RIS systems, achieving joint optimization of robustness and communication gain.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness
• Multiagent Systems: Adversarial Agents

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

There has been a growing trend toward leveraging machine learning (ML) and deep learning (DL) techniques to optimize and enhance the performance of wireless communication systems. However, limited attention has been given to the vulnerabilities of these techniques, particularly in the presence of adversarial attacks. This paper investigates the adversarial attack and defense in distributed multiple reconfigurable intelligent surfaces (RISs)-aided multiple-input multiple-output (MIMO) communication systems-based autoencoder in finite scattering environments. We present the channel propagation model for distributed multiple RIS, including statistical information driven in closed form for the aggregated channel. The symbol error rate (SER) is selected to evaluate the collaborative dynamics between the distributed RISs and MIMO communication in depth. The relationship between the number of RISs and the SER of the proposed system based on an autoencoder, as well as the impact of adversarial attacks on the system's SER, is analyzed in detail. We also propose a defense mechanism based on adversarial training against the considered attacks to enhance the model's robustness. Numerical results indicate that increasing the number of RISs effectively reduces the system's SER but leads to the adversarial attack-based algorithm becoming more destructive in the white-box attack scenario. The proposed defense method demonstrates strong effectiveness by significantly mitigating the attack's impact. It also substantially reduces the system's SER in the absence of an attack compared to the original model. Moreover, we extend the phenomenon to include decoder mobility, demonstrating that the proposed method maintains robustness under Doppler-induced channel variations.

Problem

• Investigating adversarial attacks and defense in distributed RIS-MIMO communication systems
• Analyzing how RIS quantity affects symbol error rate under adversarial attacks
• Proposing adversarial training defense to enhance system robustness and performance

Innovation

• Adversarial training defense enhances model robustness
• Distributed RIS-MIMO autoencoder optimizes SER performance
• Defense mechanism mitigates attacks under Doppler variations