ADS-B Data Attack Detection Based on Generative Adversarial Networks

With the requirements on accuracy, coverage and reliability, the air traffic surveillance is being developed into the next generation. In 2020, ADS-B data is becoming the foundation to establish air traffic situation awareness capabilities. However, ADS-B is designed without sufficient security guarantees, which results in diverse attack threats. Hence, it is in demand of effective attack detections to keep attack data away from decision flows. To improve the accuracy and robustness, attack detection based on generative adversarial network for ADS-B data is proposed. The LSTM networks are the core components to set up the generator and discriminator to make the most of temporal spatial correlations. Utilizing the reconstruction error and discriminative loss, the comprehensive detection metric is obtained to identify attack behaviors. To enhance the robustness, the analysis threshold for detection decision is determined in terms of normal data intrinsic features. By experimental analysis on real ADS-B data, the accuracy and robustness of the proposed method is validated.