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

arXiv: https://arxiv.org/abs/2506.21142 · HTML: https://mlsanity.com/paper/2506.21142/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 26 Jun 2025.

## Builds on (most cited first, 8 of 8)

- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Importance Weighted Autoencoders](https://mlsanity.com/paper/1509.00519/) (2015)
- [Understanding Measures of Uncertainty for Adversarial Example Detection](https://mlsanity.com/paper/1803.08533/) (2018)
- [Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder](https://mlsanity.com/paper/2003.02977/) (2020)
- [Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges](https://mlsanity.com/paper/1805.00881/) (2018)
- [Anomaly Detection With Conditional Variational Autoencoders](https://mlsanity.com/paper/2010.05531/) (2020)
- [GAN-based Domain Inference Attack](https://mlsanity.com/paper/2212.11810/) (2022)

## Built on by (most cited first, 1 of 1)

- [Real-Time Bayesian Detection of Drift-Evasive GNSS Spoofing in Reinforcement Learning Based UAV Deconfliction](https://mlsanity.com/paper/2507.11173/) (2025)

## Abstract (arXiv)

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.