Spoofing Attack Detection Approaches based on Indoor Channel Continuity in IEEE 802.11 Wireless Local Area Networks

This paper presents three novel spoofing attack detection approaches based on indoor wireless channel continuity, including a cross-correlation approach, a machine learning approach based on nearest neighbor and a machine learning approach based on neural network. The proposed approaches are evaluated In lab experiments in a wireless chamber. The experimental data used is the IEEE 802.11ac WiFi channel frequency response estimates from continually captured Win packets transmitted by a slowly moving mobile phone embedded with a Samsung Exynos WiFi MODEM. The experimental results show that the proposed neural network approach outperforms the other two approaches in terms of spoofing attack detection.