A Distributed RF Threat Sensing Architecture

被引:0
作者
Michalis, Georgios [1 ]
Rousias, Andreas [2 ]
Kanaris, Loizos [1 ]
Kokkinis , Akis [1 ]
Kanaris , Pantelis [1 ]
Stavrou, Stavros [2 ]
机构
[1] Sigint Solutions Ltd., Nicosia
[2] Faculty of Pure and Applied Sciences, Open University of Cyprus, Nicosia
关键词
cyber; RF jamming; RF sensing; security operations center (SOC); wireless security; wireless threats;
D O I
10.3390/info15120752
中图分类号
学科分类号
摘要
The scope of this work is to propose a distributed RF sensing architecture that interconnects and utilizes a cyber security operations center (SOC) to support long-term RF threat monitoring, alerting, and further centralized processing. For the purpose of this work, RF threats refer mainly to RF jamming, since this can jeopardize multiple wireless systems, either directly as a Denial of Service (DoS) attack, or as a means to force a cellular or WiFi wireless client to connect to a malicious system. Furthermore, the possibility of the suggested architecture to monitor signals from malicious drones in short distances is also examined. The work proposes, develops, and examines the performance of RF sensing sensors that can monitor any frequency band within the range of 1 MHz to 8 GHz, through selective band pass RF filtering, and subsequently these sensors are connected to a remote SOC. The proposed sensors incorporate an automatic calibration and time-depended environment RF profiling algorithm and procedure for optimizing RF jamming detection in a dense RF spectrum, occupied by heterogeneous RF technologies, thus minimizing false-positive alerts. The overall architecture supports TCP/IP interconnections of multiple RF jamming detection sensors through an efficient MQTT protocol, allowing the collaborative operation of sensors that are distributed in different areas of interest, depending on the scenario of interest, offering holistic monitoring by the centralized SOC. The incorporation of the centralized SOC in the overall architecture allows also the centralized application of machine learning algorithms on all the received data. © 2024 by the authors.
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