Recently, the number of devices that are connected to the Internet are increasing exponentially due to the rise of the Internet of Things (IoT) era. Despite many advancements of the IoT era, we have been exposed to cyber security threats. Moreover, in this Covid-19 pandemic situation, the trend of cyber crimes is also increasing sharply. In this paper, we discuss one of possible countermeasures to combat cyber threats, namely Intrusion Detection Systems (IDS). IDS usually leverage many different types of machine learning models to detect the unknown attacks. In order to avoid confusion for future researchers in this field, we examine several states of the art papers which leverage deep learning for IDS in Wi-Fi networks. For this purpose, we choose one common Wi-Fi networks dataset, called AWID dataset. By examining the recent studies, we are able to understand current problems of IDS in Wi-Fi networks and able to prepare the best machine learning model for the corresponding problem to achieve a safe environment with minimal risk of cyber threats. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Deep Learning Models for Intrusion Detection in Wi-Fi Networks: A Literature Survey