Convolutional neural network based evil twin attack detection in WiFi networks

Abstract

Evil Twin Attack (ETA) refers to attackers use a device to impersonate a legitimate hotspot. To address the problem of ETAs in the WiFi network, a Convolutional Neural Network (CNN) attack detection method is proposed. The method uses the preamble of the WiFi signal as the feature and uses it to train a CNN based classification model. Next, it uses the trained model to detect the potential ETA device by the inconsistent of the identity it claims and the signal feature. Experiments based on the commercial hardware demonstrate that the proposed method can effectively detect the Evil Twin Attack.