Dynamic temporal ADS-B data attack detection based on sHDP-HMM

Abstract

For the next generation air traffic surveillance, ADS-B is becoming the primary method to obtain more accurate data with wide coverage, which establishes the foundation for automatic and intelligent air traffic management system. However, ADS-B is designed without sufficient security considerations, transmitting with plain text without integrity and authentication validations. Thus, ADS-B data is in face of various attack threats, which may cause disruptions on system availability and reliability. To eliminate effects of attack behaviours, attack detection is in demand to avoid attack data injecting into decision making flow. Based on hidden Markov model with sticky hierarchical Dirichlet process, the dynamic temporal detection method is proposed to detect multiple attack patterns. Taking advantage of multiple attribute data, the dimensions of data are reduced to one dimension to set up feature sequences. With sticky hierarchical Dirichlet process, the parameters are obtained for hidden Markov model dynamically. Utilizing hidden Markov model, the generative model is established to predict hidden states of ADS-B data sequence. By analysing the contextual deviation information on hidden state sequences, the attack behaviours are discriminated to determine the attack data. By experiments on real ADS-B data, the feasibility and accuracy of the proposed method are validated.