Using Machine Learning Techniques to Detect Cyberattacks in Smart Homes: A Survey

Smart Home solutions and the usage of IoT (Internet of Things) in home automation has an increased popularity recently, and these solutions become more and more complicated and the connected devices is no longer limited to some entertainment tools that improve human life, but rather now it includes some sensitive medical tools that contribute in preserving and saving people’s lives, especially with regard to patients, the elderly and children which leads automatically to the rise in the number and types of connected smart devices and therefore it leads to an increase in the amount of transmitted data over the network, this issue raises the importance of the cybersecurity concept in smart homes and then increased the researchers’ interest in the work to detect any anomaly traffic among smart homes networks. At the same time the smart home solutions developed and become more critical the attacks scenarios and their complexity increased too, the point that imposes on researches to use different techniques in intrusion detection (ID) and attacks classifications and the Machine Learning (ML) and Deep Learning (DL) algorithms have shown promising results in this task. This paper discusses a variety of machine learning algorithms, datasets, data collection methodologies and features selection methods that have been used to secure smart home network solutions against zero-day attacks.