Improving Detection of Wi-Fi Impersonation by Fully Unsupervised Deep Learning

Intrusion Detection System (IDS) has been becoming a vital measure in any networks, especially Wi-Fi networks. Wi-Fi networks growth is undeniable due to a huge amount of tiny devices connected via Wi-Fi networks. Regrettably, adversaries may take advantage by launching an impersonation attack, a common wireless network attack. Any IDS usually depends on classification capabilities of machine learning, which supervised learning approaches give the best performance to distinguish benign and malicious data. However, due to massive traffic, it is difficult to collect labeled data in Wi-Fi networks. Therefore, we propose a novel fully unsupervised method which can detect attacks without prior information on data label. Our method is equipped by an unsupervised stacked autoencoder for extracting features and a k-means clustering algorithm for clustering task. We validate our method using a comprehensive Wi-Fi network dataset, Aegean Wi-Fi Intrusion Dataset (AWID). Our experiments show that by using fully unsupervised approach, our method is able to classify impersonation attack in Wi-Fi networks with 92% detection rate without any label needed during training.