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

arXiv: https://arxiv.org/abs/2109.04247 · HTML: https://mlsanity.com/paper/2109.04247/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 75 references that are in the map.
- First posted on arXiv 8 Sep 2021.

## Builds on (most cited first, 8 of 8)

- [LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection](https://mlsanity.com/paper/1607.00148/) (2016)
- [A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder](https://mlsanity.com/paper/1711.00614/) (2017)
- [Using LSTM Encoder-Decoder Algorithm for Detecting Anomalous ADS-B Messages](https://mlsanity.com/paper/1711.10192/) (2017)
- [VizADS-B: Analyzing Sequences of ADS-B Images Using Explainable Convolutional LSTM Encoder-Decoder to Detect Cyber Attacks](https://mlsanity.com/paper/1906.07921/) (2019)
- [Detecting ADS-B Spoofing Attacks using Deep Neural Networks](https://mlsanity.com/paper/1904.09969/) (2019)
- [Many-to-Many Voice Conversion using Cycle-Consistent Variational Autoencoder with Multiple Decoders](https://mlsanity.com/paper/1909.06805/) (2019)
- [On the Security of the Automatic Dependent Surveillance-Broadcast Protocol](https://mlsanity.com/paper/1307.3664/) (2013)
- [On Perception and Reality in Wireless Air Traffic Communications Security](https://mlsanity.com/paper/1602.08777/) (2016)

## Built on by (most cited first, 2 of 2)

- [New Machine Learning Approaches for Intrusion Detection in ADS-B](https://mlsanity.com/paper/2510.08333/) (2025)
- [Capabilities of Auto-encoders and Principal Component Analysis of the Reduction of Microstructural Images; Application on the Acceleration of Phase-Field Simulations](https://mlsanity.com/paper/2605.04229/) (2026)

## Abstract (arXiv)

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.