An Adaptive-Data-Driven Attack Detection Framework on ADS-B Data
With the variety and quantity of flights increasing, accurate and efficient surveillance methods are in great demands for the next generation air traffic management. Relying on high accuracy, wide coverage, low deployment cost and data share support, Automatic Dependent Surveillance – Broadcast (ADS-B) is becoming the primary surveillance method in 2020. However, ADS-B data is lacking of sufficient security measures to ensure data integrity and authentication, which makes it face with various attack threats. To detect the malicious data caused by attack behaviours accurately, an adaptive-data-driven attack detection framework is proposed, which is utilized to establish the consistent framework for predictive discriminant detection methods. It is composed of sequential predictor, behaviour discriminator and dynamic updater, enhancing adaptive sequential detection performances. According to the framework, an effective implementation is designed to improve attack detection accuracy: (I) The sequential predictor identifies flight phases to predict sequential data effectively and accomplish model fusion to generate ADS-B predictive data sequences. (II) The behaviour discriminator utilizes value differences and contextual information to distinguish attack data from ADS-B data sequences. (III) The dynamic updater is designed to update the training data sets and discriminate threshold dynamically, improving the adaptation in face of concept drifts for ADS-B data. By experiments on real ADS-B data with diverse attack patterns, the feasibility and efficiency of the framework are validated.