An Anomaly Detection Model for ADS-B Systems Using a LSTM-based Variational Autoencoder

Abstract

Automatic Dependent Surveillance-Broadcast (ADS-B) is an important part of the next generation air transportation system, but its protocol does not provide relevant authentication and data encryption, so it is extremely vulnerable to various spoofing attacks. Therefore, it is necessary to develop a set of ADS-B data anomaly detection models. According to the strong temporal dependence and fast update rate of ADS-B data, we initiatively introduce a novel anomaly detection model based on variational autoencoder and long short-term memory networks (VAE-LSTM). In the model, we focus on reconstructing the expected distribution of ADS-B data, which is called reconstruction probability. At the same time, to model ADS-B data with the temporal dependence, we use the LSTM networks as the main architecture of the model, which is able to make use of long-term temporal dependence and avoid the vanishing gradient problem during training. Experiments show that the proposed model has a higher area under the receiver operating characteristic curve and the precision-recall curve than the other baseline methods from the literature, and reveal that the model has better performance of anomaly detection of ADS-B data.