Generative AI-empowered Resilient Adaptive ISAC Against Adversarial Machine Learning Attacks

In this paper, we present a novel resilient adaptive integrated sensing and communication (RAd-ISAC) framework. It aims to enhance ISAC systems for advanced driver-assistance systems (ADASs) to mitigate adversarial machine learning (AML) threats and improve resource efficiency. AML attacks target ADASs by injecting false Range-Doppler maps (RDMs) to mislead the system about target vehicles (TVs), resulting in resource inefficiency. Our approach introduces a generative adversarial network (GAN) equipped with a differentiable Kolmogorov-Smirnov (KS) loss function, termed KSGAN. This significantly enhances AML attack detection by generating highly realistic RDM samples, improving the robustness of the AML detector. To optimize resource allocation in ISAC systems, we propose an adaptive signal transmission method. This allows the source vehicle (SV) to switch dynamically between ISAC and communication-only signals based on the AML detector's output and 2D constant false alarm rate (CFAR) analyses. We conduct extensive simulations with synthetic data using IBM's adversarial robustness toolbox (ART). Our results show that KSGAN outperforms standard GAN, Wasserstein GAN (WGAN), and relational GAN (RGAN) in AML detection. Additionally, when compared to other ISAC designs, including standalone ISAC, Faster-than-Nyquist ISAC (FTN-ISAC), ISAC-accelerated edge intelligence system, and hybrid-ISAC, our Rad-ISAC framework achieves the lowest root mean square error (RMSE) and Cramér-Rao lower bound (CRLB). This work highlights an unexplored vulnerability of ISAC systems to AML attacks and demonstrates advancements in ADAS vehicle safety and efficiency.