X-ZeroSec: Zero-Shot Evasion Attack Detection in Deep Learning-Based Beamforming Prediction With XAI

Abstract

With the rise of next-generation wireless systems, Deep Learning (DL)-based security solutions are gaining widespread adoption. One key area DL impacts is beamforming prediction in mmWave communication. This study investigates how vulnerable AI-based models are to adversarial attacks, which can disrupt network performance in complex distributed MIMO (D-MIMO) environments. Since AI plays a crucial role in selecting beams efficiently, such vulnerabilities pose a serious threat to the reliability of 5G and 6G networks. Existing work fails in achieving generalizable performance on unseen attacks in low-latency requirements. To enhance resilience, we propose an innovative XAI-based framework dubbed X-ZeroSec that leverages SHAP, specifically Shapley values from cooperative game theory, to detect and mitigate adversarial attacks. Our approach introduces the concept of explanation distillation, where feature attributions from a “teacher” detector model train a generalized XAI-based “student” model, yielding improved and generalized detection rates, especially for zero-shot attacks. The framework enhances robustness against various adversarial patterns, achieving over 10-16% improved accuracy compared to state-of-the-art detectors. Comprehensive evaluations demonstrate that integrating XAI into AI-driven beamforming fortifies model defenses, preserving mmWave system performance while meeting evolving security requirements. X-ZeroSec framework supports diverse DL-based applications and it is implemented on the UCD NETSLAB 5G O-RAN testbed, demonstrating promising end-to-end latency of less than 4 milliseconds.