Jointly beam stealing attackers detection and localization without training: an image processing viewpoint

Abstract

Recently revealed beam stealing attacks could greatly threaten the security and privacy of IEEE 802.11ad communications. The premise to restore normal network service is detecting and locating beam stealing attackers without their cooperation. Current consistency-based methods are only valid for one single attacker and are parameter-sensitive. From the viewpoint of image processing, this paper proposes an algorithm to jointly detect and locate multiple beam stealing attackers based on RSSI (Received Signal Strength Indicator) map without the training process involved in deep learning-based solutions. Firstly, an RSSI map is constructed based on interpolating the raw RSSI data for enabling high-resolution localization while reducing monitoring cost. Secondly, three image processing steps, including edge detection and segmentation, are conducted on the constructed RSSI map to detect and locate multiple attackers without any prior knowledge about the attackers. To evaluate our proposal’s performance, a series of experiments are conducted based on the collected data. Experimental results have shown that in typical parameter settings, our algorithm’s positioning error does not exceed 0.41 m with a detection rate no less than 91%.

Graphical abstract

Keywords

beam-stealing attacks / detection / localization / image processing

Yaoqi Yang received the MS degree from the College of Communications Engineering, Army Engineering University of PLA, China in 2021. His research interests include wireless communications systems and signal processing

Xianglin Wei received the PhD degree from the PLA University of Science and Technology, China in 2012. He is currently working as a Researcher with the 63rd Research Institute, National University of Defense Technology, China. His research interests include mobile edge computing and wireless network optimization

Renhui Xu received the PhD degree in communications and information systems from Southeast University, Nanjing, China in 2010. He is currently an Associate Professor with the College of Communications Engineering, Army Engineering University of PLA, China. His research interests include signal processing, and wireless ad hoc networks

Weizheng Wang received the BS degree in software engineering from Yangzhou University, China in 2019, the MS degrees in computer science and engineering from the University of Aizu, Aizu-Wakamatsu, Japan in 2021. Now he is a Research Associate in University of Aizu and pursuing the PhD degree at the Department of Computer Science, City University of Hong Kong, China. His research interests include blockchain technology and IoT system

Laixian Peng received the BS and PhD degrees in telecom engineering from the Nanjing Institute of Communications Engineering, China in 1999 and 2004, respectively. His research interests include high-speed switching architectures and ad hoc networks and applications. He was a recipient of the Excellence PhD Thesis Award of Jiangsu Province, in 2005

Yangang Wang is currently working as a Researcher with the 63rd Research Institute, National University of Defense Technology, China. His research interests include mobile edge computing and wireless network optimization

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant No. 61671471).

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