Taxonomy of deep learning-based intrusion detection system approaches in fog computing: a systematic review

Abstract

The Internet of Things (IoT) has been used in various aspects. Fundamental security issues must be addressed to accelerate and develop the Internet of Things. An intrusion detection system (IDS) is an essential element in network security designed to detect and determine the type of attacks. The use of deep learning (DL) shows promising results in the design of IDS based on IoT. DL facilitates analytics and learning in the dynamic IoT domain. Some deep learning-based IDS in IOT sensors cannot be executed, because of resource restrictions. Although cloud computing could overcome limitations, the distance between the cloud and the end IoT sensors causes high communication costs, security problems and delays. Fog computing has been presented to handle these issues and can bring resources to the edge of the network. Many studies have been conducted to investigate IDS based on IoT. Our goal is to investigate and classify deep learning-based IDS on fog processing. In this paper, researchers can access comprehensive resources in this field. Therefore, first, we provide a complete classification of IDS in IoT. Then practical and important proposed IDSs in the fog environment are discussed in three groups (binary, multi-class, and hybrid), and are examined the advantages and disadvantages of each approach. The results show that most of the studied methods consider hybrid strategies (binary and multi-class). In addition, in the reviewed papers the average Accuracy obtained in the binary method is better than the multi-class. Finally, we highlight some challenges and future directions for the next research in IDS techniques.