Virtual MAC Spoofing Detection through Deep Learning

Peng Jiang, Hongyi Wu, Cong Wang, Chunsheng Xin

Abstract: Identity-based attacks such as MAC spoofing are common in wireless networks. The recently developed virtualization technologies bring a new type of MAC spoofing attack, virtual MAC spoofing. This makes it even more challenging to detect such attacks, especially in a tight environment with spatial similarities. In this paper, we design, implement and evaluate a system to effectively detect virtual MAC spoofing attacks via deep learning. A deep convolutional neural network is constructed to extract physical features from CSI obtained from packet transmissions, to detect virtual MAC spoofing attacks. An important merit of the proposed detection system is that this system can distinguish two devices even at the same location, which was not well addressed by previous approaches. Our extensive experimental results demonstrate the effectiveness of the system with an average detection accuracy of 95%, even when devices are co-located.