DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation

TL;DR

This paper introduces DAE, a novel multivariate anomaly detection model for air traffic surveillance data, which outperforms existing models in accuracy and speed by leveraging phase-specific decoders within an auto-encoder framework.

Contribution

The paper presents DAE, a new auto-encoder based model with multiple decoders for different flight phases, improving anomaly detection in air traffic data.

Findings

DAE outperforms existing models in detection accuracy.

DAE achieves faster anomaly detection.

The dataset and code are publicly available for replication.

Abstract

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.