基于深度学习的ADS-B异常数据检测模型

doi:10.7527/S1000-6893.2019.23220

Abstract

Abstract: Automatic Dependent Surveillance-Broadcast (ADS-B) is an important part of the next generation air transportation system. It is a critical communication and monitoring technology in the new navigation system, but its protocol does not provide relevant authentication and data encryption, so it is extremely vulnerable to various spoofing attack. Based on the data characteristics, this paper uses the deep learning seq2seq model to reconstruct the ADS-B time series, and the reconstruction error can detect the anomalous ADS-B messages. Extending the feature space of time series enables the model to better capture the time dependence to further improve the effect of anomaly detection. The experimental results show that the proposed method is superior to traditional machine learning methods and time series enrichment can improve detection results. Compared with the existing spoofing attack detection method, the proposed method does not need to change the ADS-B protocol and does not require additional participating nodes or sensors, and has certain adaptability and flexibility.

Key words: Automatic Dependent Surveillance-Broadcast (ADS-B), security, anomaly detection, deep learning, seq2seq model

CLC Number:

V243
TP277

DING Jianli, ZOU Yunkai, WANG Jing, WANG Huaichao. ADS-B anomaly data detection model based on deep learning[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2019, 40(12): 323220-323220.

References 28

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ADS-B anomaly data detection model based on deep learning

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