ADS-B Message Injection Attack on UAVs: Assessment of SVM-based Detection Techniques
Hadjar Ould Slimane, Selma Benouadah, Khair Al Shamaileh, V. Devabhaktuni, Naima Kaabouch
Abstract
As more aircraft are using the Automatic Dependent Surveillance-Broadcast (ADS-B) devices for navigation and surveillance, the risks of injection attacks are highly increasing. The exchanged ADS-B messages are neither encrypted nor authenticated while containing valuable operational information, which imposes high risk on the safety of the airspace. For this reason, we propose in this paper an SVM-based ADS-B message injection attack detection technique for UAV onboard implementation. First, we simulated several message injection attacks on real raw ADS-B data. Then, three Support Vector Machine (SVM) models were examined in terms of two types of assessment criteria, detection efficiency and model performance. The results show that the C-SVM model is the best fit for our application, with an accuracy of 95.32%.