RadArnomaly: Protecting Radar Systems from Data Manipulation Attacks
Radar systems are mainly used for tracking aircraft, missiles, satellites, and watercraft. In many cases, information regarding the objects detected by a 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. T...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/11/4259 |
| _version_ | 1797491585937571840 |
|---|---|
| author | Shai Cohen Efrat Levy Avi Shaked Tair Cohen Yuval Elovici Asaf Shabtai |
| author_facet | Shai Cohen Efrat Levy Avi Shaked Tair Cohen Yuval Elovici Asaf Shabtai |
| author_sort | Shai Cohen |
| collection | DOAJ |
| description | Radar systems are mainly used for tracking aircraft, missiles, satellites, and watercraft. In many cases, information regarding the objects detected by a 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 have 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 an unsupervised deep-learning-based method for detecting anomalies in radar system data streams; we take into consideration the fact that a data stream created by a radar system is heterogeneous, i.e., it contains both numerical and categorical features with non-linear and complex relationships. We propose a novel technique that learns the correlation between numerical features and an embedding representation of categorical features in an unsupervised manner. The proposed technique, which allows for the detection of the malicious manipulation of critical fields in a data stream, is complemented by a timing-interval anomaly-detection mechanism proposed for the detection of message-dropping attempts. Real radar system data were used to evaluate the proposed method. Our experiments demonstrated the method’s high detection accuracy on a variety of data-stream manipulation attacks (an average detection rate of 88% with a false -alarm rate of 1.59%) and message-dropping attacks (an average detection rate of 92% with a false-alarm rate of 2.2%). |
| first_indexed | 2024-03-10T00:51:25Z |
| format | Article |
| id | doaj.art-e796816c59e14db2b8e85d7d18c1e44a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T00:51:25Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e796816c59e14db2b8e85d7d18c1e44a2023-11-23T14:51:06ZengMDPI AGSensors1424-82202022-06-012211425910.3390/s22114259RadArnomaly: Protecting Radar Systems from Data Manipulation AttacksShai Cohen0Efrat Levy1Avi Shaked2Tair Cohen3Yuval Elovici4Asaf Shabtai5Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Be’er Sheva 8410501, IsraelDepartment of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Be’er Sheva 8410501, IsraelCyber Division, Elta Company, Ashdod 7710202, IsraelCyber Division, Elta Company, Ashdod 7710202, IsraelDepartment of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Be’er Sheva 8410501, IsraelDepartment of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Be’er Sheva 8410501, IsraelRadar systems are mainly used for tracking aircraft, missiles, satellites, and watercraft. In many cases, information regarding the objects detected by a 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 have 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 an unsupervised deep-learning-based method for detecting anomalies in radar system data streams; we take into consideration the fact that a data stream created by a radar system is heterogeneous, i.e., it contains both numerical and categorical features with non-linear and complex relationships. We propose a novel technique that learns the correlation between numerical features and an embedding representation of categorical features in an unsupervised manner. The proposed technique, which allows for the detection of the malicious manipulation of critical fields in a data stream, is complemented by a timing-interval anomaly-detection mechanism proposed for the detection of message-dropping attempts. Real radar system data were used to evaluate the proposed method. Our experiments demonstrated the method’s high detection accuracy on a variety of data-stream manipulation attacks (an average detection rate of 88% with a false -alarm rate of 1.59%) and message-dropping attacks (an average detection rate of 92% with a false-alarm rate of 2.2%).https://www.mdpi.com/1424-8220/22/11/4259radar systemanomaly detectiondeep learning |
| spellingShingle | Shai Cohen Efrat Levy Avi Shaked Tair Cohen Yuval Elovici Asaf Shabtai RadArnomaly: Protecting Radar Systems from Data Manipulation Attacks Sensors radar system anomaly detection deep learning |
| title | RadArnomaly: Protecting Radar Systems from Data Manipulation Attacks |
| title_full | RadArnomaly: Protecting Radar Systems from Data Manipulation Attacks |
| title_fullStr | RadArnomaly: Protecting Radar Systems from Data Manipulation Attacks |
| title_full_unstemmed | RadArnomaly: Protecting Radar Systems from Data Manipulation Attacks |
| title_short | RadArnomaly: Protecting Radar Systems from Data Manipulation Attacks |
| title_sort | radarnomaly protecting radar systems from data manipulation attacks |
| topic | radar system anomaly detection deep learning |
| url | https://www.mdpi.com/1424-8220/22/11/4259 |
| work_keys_str_mv | AT shaicohen radarnomalyprotectingradarsystemsfromdatamanipulationattacks AT efratlevy radarnomalyprotectingradarsystemsfromdatamanipulationattacks AT avishaked radarnomalyprotectingradarsystemsfromdatamanipulationattacks AT taircohen radarnomalyprotectingradarsystemsfromdatamanipulationattacks AT yuvalelovici radarnomalyprotectingradarsystemsfromdatamanipulationattacks AT asafshabtai radarnomalyprotectingradarsystemsfromdatamanipulationattacks |