Dual-Impact Feature Selection for Adversarially Robust, Functionality-Preserving UAV Intrusion Detection

The increasing deployment of Unmanned Aerial Vehicles (UAVs) in critical operations exposes them to cyberattacks. Although deep learning-based Intrusion Detection Systems (IDSs) are effective, they are susceptible to adversarial attacks that manipulate input features to avoid detection. Conventional feature selection methods do not distinguish between features critical to model accuracy and those essential for preserving cyberattack operational validity. To address this, we propose a Dual-Impact Feature Selection (DIFS) framework for robust UAV-IDS models. Our approach evaluates features based on two criteria: the first is Model Performance Impact (MPI), using Integrated Gradients (IG) and Local Interpretable Model-agnostic Explanations (LIME) to measure feature influence on detection accuracy, and the second is Functionality Preservation Criterion (FPC), a clustering-based method that assesses whether a feature is indispensable for cyberattack execution. Features with high MPI and FPC are identified as Dual-Impact Features (DIFs). We generate constrained adversarial attacks that perturb these DIFs to create realistic evasion samples. Using these samples for adversarial training, we develop three robust UAV-IDS Convolutional Neural Network (CNN) models. Evaluated on three UAV network intrusion datasets, our framework demonstrates improved resilience. The models achieve up to 99.8% detection accuracy while reducing Attack Success Rate (ASR) to as low as 0.002, supporting their potential for designing adversary-resistant detection systems for UAV networks.