A systematic literature review of recent lightweight detection approaches leveraging machine and deep learning mechanisms in Internet of Things networks

The Internet of Things (IoT) connects daily use devices to the Internet, such as home appliances, health care equipment, sensors, and industrial devices. Concurrently, numerous cyber-attacks target those objects and their backbone IoT networks consecutively. Therefore, several researchers have adopted Machine Learning (ML) and Deep Learning (DL) algorithms to develop efficient Intrusion Detection Systems (IDSs). However, the restricted resources of IoT devices hinder integrating those systems with those tiny devices. Hence, designing lightweight IDSs gets more interest from researchers to build efficient detection models to discard attacks in IoT networks. To give a holistic insight into this research domain, this paper presents a Systematic Literature Review (SLR) to review and analyse the recent ML and DL techniques to lighten the IDS models for detecting attacks in IoT devices. In addition, the literature studies were retrieved from six scientific databases Google Scholar, Science Direct, IEEE Xplore®, Scopus, Web of Science, and Springer. From 4,703 identified records, 57 studies were adopted based on predesigned research questions and inclusion/exclusion criteria. The study's findings illustrate the most recently used ML and DL mechanisms and feature engineering techniques to lighten the proposed IDS models. It also shows the most attacks detected, datasets used, tools and network simulators employed, and evaluation metrics and parameters. Furthermore, it suggests the research challenges and future direction after discussing the limitations of the currently proposed techniques. This study shows that most selected studies are journal articles published in IEEE Xplore®. Furthermore, the most used feature engineering techniques are filter-based, as they deliver better performance and lightness than the developed models. Most studies use correlation algorithms as a feature selection technique. Finally, the most discussed attack in the selected studies is the DoS attack.