A cGAN Empowered Physical Layer Authentication Against Malicious RIS Attacks
2026 IEEE International Conference on Communications, ICC 2026, Glasgow, İngiltere, 24 - 28 Mayıs 2026, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/icc59461.2026.11586967
- Basıldığı Şehir: Glasgow
- Basıldığı Ülke: İngiltere
- Anahtar Kelimeler: cGAN, CSI, mMalicious RIS, pPhysical layer authentication, RIS, sSpoofing attack
- İstanbul Medipol Üniversitesi Adresli: Evet
Özet
Reconfigurable intelligent surfaces (RIS) have emerged as a transformative technology for next-generation wireless networks, offering unprecedented control over radio propagation environments. However, their passive nature and ease of deployment introduces security vulnerabilities that remain largely unexplored. This paper investigates a spoofing attack where a malicious RIS strategically manipulates its reflection coefficients to impersonate a legitimate RIS, thereby deceiving the base station (BS) and gaining unauthorized network access. To counter this threat, we propose a novel authentication framework that formulates the detection problem as a data-driven binary classification task, leveraging conditional generative adversarial networks (cGAN). The framework employs a U-Net-based generator to synthesize realistic attack scenarios during training, while the discriminator serves as a lightweight authenticator enabling robust authentication without requiring apriori knowledge of attacker strategies. Through extensive simulations across diverse attack scenarios, including co-located and correlated configurations, we demonstrate that the trained discriminator achieves 96.4% detection accuracy against malicious RIS attackers positioned near the BS (co-located) and maintains 86.2% accuracy under correlated attack conditions.