Generative adversarial evasion and out-of-distribution detection for UAV cyber-attacks

Abstract

As UAVs are increasingly integrated into civilian airspace, the need for resilient intrusion detection system (IDS) frameworks grow, as traditional anomaly detection methods often struggle to detect novel threats. A common strategy is to treat the unfamiliar attacks as out-of-distribution (OOD) samples; hence, inadequate mitigation responses can leave systems vulnerable, granting adversaries the capability to cause potential damage. Furthermore, conventional OOD detectors frequently fail to discriminate the stealthy adversarial attacks from OOD samples. This paper proposes a conditional generative adversarial network (cGAN)-based framework specifically designed to craft stealthy adversarial attacks that effectively evade IDS mechanisms. Initially, we construct a robust multi-class classifier as IDS which classifies the benign UAV telemetry data from known cyber-attack types, including Denial of Service (DoS), false data injection (FDI), man-in-the-middle (MiTM), and replay attacks. Leveraging this classifier, our proposed cGAN strategically perturbs known attack features, generating sophisticated adversarial samples engineered to evade detection through benign misclassification. Then, the generative stealthy adversarial samples are refined to match the distribution of the OOD samples while ensuring high attack success. To effectively detect these stealthy adversarial perturbations, a conditional variational autoencoder (CVAE) is implemented, using negative log-likelihood as a metric to distinguish adversarial samples from genuine OOD samples. Comparative analyses between CVAE-based regret analysis and traditional Mahalanobis distance-based detectors demonstrate that the CVAE’s negative log-likelihood significantly outperforms in detecting stealthy adversarial attacks from OOD samples. Our findings highlight the necessity of advanced probabilistic modeling techniques to reliably detect and adapt the existing IDS against novel, generative-model-based stealthy cyber threats.