Classification of Radar Signals with Convolutional Neural Networks

Hong, Seok‐Jun; Yi, Yearn-Gui; Jo, Jeil; Seo, Bo-Seok

doi:10.1109/icufn.2018.8436647

Cited by 9 publications

(7 citation statements)

References 2 publications

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“…Most studies that used machine learning techniques to protect radar systems from attacks focused on protecting the systems from jamming attacks. Some studies proposed jamming classification methods [10,17,20,22], while others proposed jamming mitigation strategies [11,14]. In addition, a comprehensive survey discussing all of these methods in detail was published [13].…”

Section: Related Workmentioning

confidence: 99%

RadArnomaly: Protecting Radar Systems from Data Manipulation Attacks

Cohen¹,

Levy²,

Shaked³

et al. 2021

Preprint

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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). CCS CONCEPTS• Security and privacy → Intrusion detection systems; • Computing methodologies → Neural networks.

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Section: Related Workmentioning

confidence: 99%

RadArnomaly: Protecting Radar Systems from Data Manipulation Attacks

Cohen¹,

Levy²,

Shaked³

et al. 2021

Preprint

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“…Once the signals transmitted from the radar are successfully recognised, the information is then utilised for intelligent adaptive jamming [46, 47]. Alternatively, the ARC may also create fake returns using the recognised signals transmitted from the enemy radars to misguide them.…”

Section: Four Types Of Radar Systems Constituting the Mrnmentioning

confidence: 99%

Moving target detection and tracking with multiplatform radar network (MRN)

Geng

Wang

et al. 2021

IET Radar Sonar & Navi

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A multiplatform radar network (MRN) composed of multiple transmitting and receiving facilities is advantageous over monostatic radar in moving target detection (MTD) and tracking due to the diversity gain. Within the framework of the Internet of Battlefield Things (IoBT), the authors propose that the airborne platforms equipped with the cognitive multimodal radar (CMR), the subarray‐based MIMO radar (SMR), the active electronically scanned array (AESA) radar, and the adaptive radar countermeasures (ARC) should team up in target detection and tracking. To obtain better target detection and tracking performance, the probing signals from the CMR, the SMR, and the AESA radar are adaptively optimised based on the scenario under consideration. The ARC consists of two parts: 1) RF signal sensing and recognition with deep neural network (DNN), that is, to detect, identify, and localise multiple radio frequency emitters; 2) intelligent fake return creation and jamming to confuse the enemy radars in real time in the field. This work aims to serve as a good reference for future researchers interested in developing MRN with diverse nodes and the general teaming of radars on the battlefield.

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“…The type of radar chosen for our study is the conventional pulsed radar which is widely used in ESM for searching, detecting and tracking airborne targets [30, 34]. The pulsed radar ASP is characterised by the envelope of the received signal strength at each time stamp (TOA) as obtained from the PA.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…The pulsed radar ASP is characterised by the envelope of the received signal strength at each time stamp (TOA) as obtained from the PA. The PA is related to the radar signal power, its beam shape and antenna scanning features [34]. Radar ASP is determined by the relative angular position of the RWR with respect to the radar main lobe with the passage of time and proves to be a substitute parameter in the absence of DOA information.…”

Section: Theoretical Backgroundmentioning

confidence: 99%