Jointly beam stealing attackers detection and localization without training:an image processing viewpoint
作者机构：College of Communication EngineerArmy Engineering University of PLANanjing 210000China The 63rd Research InstituteNational University of Defense TechnologyNanjing 210007China Department of Computer ScienceCity University of Hong KongHong Kong 999077China
出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)
年 卷 期：2023年第17卷第3期
页 面：145-160页
核心收录：
学科分类：0710[理学-生物学] 0810[工学-信息与通信工程] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：beam-stealing attacks detection localization image processing
摘 要：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 parametersensitive.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%.
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