  The Automatic Dependent Surveillance Broadcast protocol is one of the latest
compulsory advances in air surveillance. While it supports the tracking of the
ever-growing number of aircraft in the air, it also introduces cybersecurity
issues that must be mitigated e.g., false data injection attacks where an
attacker emits fake surveillance information. The recent data sources and tools
available to obtain flight tracking records allow the researchers to create
datasets and develop Machine Learning models capable of detecting such
anomalies in En-Route trajectories. In this context, we propose a novel
multivariate anomaly detection model called Discriminatory Auto-Encoder (DAE).
It uses the baseline of a regular LSTM-based auto-encoder but with several
decoders, each getting data of a specific flight phase (e.g. climbing, cruising
or descending) during its training.To illustrate the DAE's efficiency, an
evaluation dataset was created using real-life anomalies as well as
realistically crafted ones, with which the DAE as well as three anomaly
detection models from the literature were evaluated. Results show that the DAE
achieves better results in both accuracy and speed of detection. The dataset,
the models implementations and the evaluation results are available in an
online repository, thereby enabling replicability and facilitating future
experiments.
