# RadArnomaly: Protecting Radar Systems from Data Manipulation Attacks

arXiv: https://arxiv.org/abs/2106.07074 · HTML: https://mlsanity.com/paper/2106.07074/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 13 Jun 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection](https://mlsanity.com/paper/1802.09089/) (2018)
- [N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders](https://mlsanity.com/paper/1805.03409/) (2018)
- [Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Network](https://mlsanity.com/paper/1703.09752/) (2017)
- [Using LSTM Encoder-Decoder Algorithm for Detecting Anomalous ADS-B Messages](https://mlsanity.com/paper/1711.10192/) (2017)
- [Decentralized Automotive Radar Spectrum Allocation to Avoid Mutual Interference Using Reinforcement Learning](https://mlsanity.com/paper/2001.02629/) (2020)
- [Detecting ADS-B Spoofing Attacks using Deep Neural Networks](https://mlsanity.com/paper/1904.09969/) (2019)
- [A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing](https://mlsanity.com/paper/2009.13702/) (2020)
- [A Brief Survey on Autonomous Vehicle Possible Attacks, Exploits and Vulnerabilities](https://mlsanity.com/paper/1810.04144/) (2018)

## Abstract (arXiv)

Radar systems are mainly used for tracking aircraft, missiles, satellites, and watercraft. In many cases, information regarding the objects detected by the radar system is sent to, and used by, a peripheral consuming system, such as a missile system or a graphical user interface used by an operator. Those systems process the data stream and make real-time, operational decisions based on the data received. Given this, the reliability and availability of information provided by radar systems has grown in importance. Although the field of cyber security has been continuously evolving, no prior research has focused on anomaly detection in radar systems. In this paper, we present a deep learning-based method for detecting anomalies in radar system data streams. We propose a novel technique which learns the correlation between numerical features and an embedding representation of categorical features in an unsupervised manner. The proposed technique, which allows the detection of malicious manipulation of critical fields in the data stream, is complemented by a timing-interval anomaly detection mechanism proposed for the detection of message dropping attempts. Real radar system data is used to evaluate the proposed method. Our experiments demonstrate the method's high detection accuracy on a variety of data stream manipulation attacks (average detection rate of 88% with 1.59% false alarms) and message dropping attacks (average detection rate of 92% with 2.2% false alarms).