RadArnomaly: Protecting Radar Systems from Data Manipulation Attacks
Shai Cohen, Efrat Levy, Avi Shaked, Tair Cohen, Yuval, Elovici, Asaf Shabtai

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…
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Network Security and Intrusion Detection · Artificial Immune Systems Applications
