On the Study of Joint Naive Bayes and Multi-Layer Perception for Detecting GPS Jamming in UAS

Unmanned aerial systems (UAS) and unmanned aerial vehicles (UAVs) have been utilized in numerous civilian and military applications. However, with the increasing deployment of UAS, several cybersecurity challenges have emerged, particularly vulnerabilities in GPS transmissions. GPS jamming attacks pose a significant threat to UAS, resulting in performance degradation and capability loss. To address this challenge, we propose a novel approach for detecting jamming attacks on GPS transmissions in UAS. The proposed approach combines Naive Bayes and Multi-Layer Perceptron algorithms to offer efficient and precise detection of GPS jamming attacks. We evaluated the proposed approach’s performance against various machine learning (ML) models using a synthetic dataset. The experimental results demonstrate the effectiveness of the proposed approach in detecting jamming attacks in GPS transmissions, achieving better accuracy than other ML models under several jammers. Index Terms---AI, ML, UAS/UAV, GPS Jamming Attacks.