Generative Adversarial Evasion and Out-of-Distribution Detection for UAV Cyber-Attacks

TL;DR

This paper presents a novel cGAN-based method to generate stealthy adversarial UAV cyber-attacks and a CVAE-based detector that outperforms traditional methods in identifying these sophisticated threats.

Contribution

It introduces a cGAN framework for creating stealthy adversarial attacks on UAV IDS and a CVAE-based detection method that effectively distinguishes these attacks from genuine OOD samples.

Findings

cGAN-generated attacks successfully evade IDS detection

CVAE-based detector outperforms Mahalanobis distance in identifying adversarial samples

Advanced probabilistic modeling enhances UAV cyber-attack detection

Abstract

The growing integration of UAVs into civilian airspace underscores the need for resilient and intelligent intrusion detection systems (IDS), as traditional anomaly detection methods often fail to identify novel threats. A common approach treats unfamiliar attacks as out-of-distribution (OOD) samples; however, this leaves systems vulnerable when mitigation is inadequate. Moreover, conventional OOD detectors struggle to distinguish stealthy adversarial attacks from genuine OOD events. This paper introduces a conditional generative adversarial network (cGAN)-based framework for crafting stealthy adversarial attacks that evade IDS mechanisms. We first design a robust multi-class IDS classifier trained on benign UAV telemetry and known cyber-attacks, including Denial of Service (DoS), false data injection (FDI), man-in-the-middle (MiTM), and replay attacks. Using this classifier, our cGAN perturbs known attacks to generate adversarial samples that misclassify as benign while retaining statistical resemblance to OOD distributions. These adversarial samples are iteratively refined to achieve high stealth and success rates. To detect such perturbations, we implement a conditional variational autoencoder (CVAE), leveraging negative log-likelihood to separate adversarial inputs from authentic OOD samples. Comparative evaluation shows that CVAE-based regret scores significantly outperform traditional Mahalanobis distance-based detectors in identifying stealthy adversarial threats. Our findings emphasize the importance of advanced probabilistic modeling to strengthen IDS capabilities against adaptive, generative-model-based cyber intrusions.