A Highly Interpretable Framework for Generic Low-Cost UAV Attack Detection
Shihao Wu, Yang Li, Zhaoxuan Wang, Zheng Tan, Quan Pan
Abstract
The increasing prevalence of cyber-attacks on unmanned aerial vehicles (UAVs) has led to research on effective detection methods. However, current approaches often lack transferability and interoperability, which limits their effectiveness. This study proposes a CNN-BiLSTM-Attention (CBA) model for efficient attack detection using real-time UAV sensor data. Additionally, the SHapley Additive exPlanations (SHAP) method is used to improve the interpretability of the model. The proposed approach is tested on real attack scenarios, including denial-of-service (DoS) attacks and global positioning system (GPS) spoofing attacks, and demonstrates both effectiveness and interpretability.
Topics & Concepts
InterpretabilityComputer scienceInteroperabilityIntrusion detection systemDenial-of-service attackGlobal Positioning SystemTransferabilitySpoofing attackComputer securityAnomaly detectionData miningReal-time computingArtificial intelligenceMachine learningTelecommunicationsOperating systemLogitWorld Wide WebThe InternetAnomaly Detection Techniques and ApplicationsUAV Applications and OptimizationNetwork Security and Intrusion Detection