Identity-Based Attack Detection and Classification Utilizing Reciprocal RSS Variations in Mobile Wireless Networks


One of the most dangerous dangers that wireless networks face is that of identity-based attacks, also known as IBAs. In recent times, there has been a growing interest in making use of the received signal strength, abbreviated as RSS, to identify IBAs in wireless networks. However, the currently available schemes have a propensity to generate an excessive number of false alarms when applied to the mobile scenario. In this paper, we propose a more robust Reciprocal Channel Variation-based Identification and classification (RCVIC) scheme for mobile wireless networks. This scheme takes advantage of the reciprocity of the wireless fading channel and the RSS variations that are naturally incurred by mobility in order to improve the detection performance. In contrast to existing schemes, which only look for IBAs, the RCVIC scheme uses multiple stages in its detection processes. In the event that the IBAs are found, the RCVIC scheme will split the received frames into two distinct classes. The frames that belong to the same class should be sent by the same senders. This will help with further analysis, such as network forensics, attacker localization, and trajectory analysis, amongst other things. The practicability of RCVIC is assessed using numerical data derived from theoretical research and computational simulations. Experiments conducted with off-the-shelf 802.11 devices under a variety of different attacking patterns in real indoor and outdoor mobile scenarios further validate it.

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