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

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

6 Scopus Citations
View graph of relations

Author(s)

  • Yaoqi Yang
  • Xianglin Wei
  • Renhui Xu
  • Laixian Peng
  • Yangang Wang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number173704
Journal / PublicationFrontiers of Computer Science
Volume17
Issue number3
Online published2 Nov 2022
Publication statusPublished - Jun 2023

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%.

Research Area(s)

  • beam-stealing attacks, detection, image processing, localization

Citation Format(s)

Jointly beam stealing attackers detection and localization without training: an image processing viewpoint. / Yang, Yaoqi; Wei, Xianglin; Xu, Renhui et al.
In: Frontiers of Computer Science, Vol. 17, No. 3, 173704, 06.2023.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review