Detecting Active Attacks in WiFi Network by Semi-supervised Deep Learning1)

WiFi network usage is increased rapidly these days while the number of attacks in WiFi network are growing as well. Intrusion Detection System (IDS) is one of the popular defense mechanisms that often uses e.g., machine learning algorithms in order to detect both known and unknown attacks in a particular network. We leverage an unsupervised deep learning approach, so called Stacked Auto Encoder (SAE) as feature extraction scheme. Feature extraction by SAE can reduce the complexity of original features of the dataset. While regression layer with softmax activation function is implemented as supervised classification. In this paper, we test our proposed IDS using AWID dataset which is one of comprehensive WiFi network traces from real network. Our experiments show that our proposed IDS can outperform the previous work by Kolias et al. In addition, we provide several suggestions in order to made our proposed IDS reach an optimum result.