Sequential collaborative detection strategy on ADS-B data attack

Abstract

Automatic Dependent Surveillance - Broadcast (ADS-B) surveillance is regarded as the core technology in the next generation air traffic management. Due to the absence of consideration on security, ADS-B data is faced with various challenges on integrity and authentication, especially for ADS-B data attack with high concealment. In this paper, common attack pattern models are analyzed. In terms of sequential ADS-B data, detection methods are designed according to flight and ground station capabilities, which integrate several detection methods, including flight plan validation, single node data detection and group data detection, to generate comprehensive attack probability as reference for judgment on data attack. To improve the positive detection ratio, ground to ground, ground to air and air to air collaborative detections are proposed to enhance each single node detection ability. Experiments conducted on real ADS-B data illustrated that the sequential collaborative detection strategy was efficient on effectiveness and accuracy, especially for random deviation injection attack, constant deviation injection attack and DoS attack.