RadArnomaly: Protecting Radar Systems from Data Manipulation Attacks
- URL: http://arxiv.org/abs/2106.07074v1
- Date: Sun, 13 Jun 2021 19:16:37 GMT
- Title: RadArnomaly: Protecting Radar Systems from Data Manipulation Attacks
- Authors: Shai Cohen and Efrat Levy and Avi Shaked and Tair Cohen and Yuval
Elovici and Asaf Shabtai
- Abstract summary: We present a deep learning-based method for detecting anomalies in radar system data streams.
The proposed technique allows the detection of malicious manipulation of critical fields in the data stream.
Our experiments demonstrate the method's high detection accuracy on a variety of data stream manipulation attacks.
- Score: 40.736632681576786
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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).
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