RadArnomaly: Protecting Radar Systems from Data Manipulation Attacks

TL;DR

This paper introduces a deep learning approach for detecting data manipulation and message dropping attacks in radar systems, enhancing cybersecurity by ensuring data integrity and operational reliability.

Contribution

It presents a novel unsupervised deep learning technique that models correlations in radar data streams for anomaly detection, addressing a gap in radar cybersecurity research.

Findings

Achieved 88% detection rate for data manipulation attacks

Achieved 92% detection rate for message dropping attacks

Maintained low false alarm rates (around 1.6-2.2%)

Abstract

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).