ML-Based IDPS Enhancement With Complementary Features for Home IoT Networks.

The Internet of Things (IoT) networks are obstructed by security vulnerabilities that hackers can leverage to operate intrusions in many environments, such as smart homes, smart factories, and smart healthcare systems. To overcome this obstruction, researchers have come up with different intrusion detection and prevention systems (IDPSs). Out of all the implemented technologies, Machine Learning (ML) has emerged as the most promising approach. Therefore, to improve the detection accuracy, most ML-based intrusion detection solutions focus only on investigating appropriate ML algorithms. Yet, the limitations in terms of detection accuracy in various attacks are often caused by lack of appropriate detection features. Moreover, the majority of the previous works lack intrusion prevention mechanisms and deployment architectures. Thus, in this research, we study the properties of different smart home security attacks and the quality of the features that can be brought out and employed in ML algorithms to detect each of these attacks efficiently. Furthermore, this research proposes effective intrusion prevention mechanisms and a Software-Defined Networking (SDN) based deployment architecture of the IDPSs within home networks. Experimental evaluations of the proposed solution are provided using different feature sets and various ML models. The contributions and advancements discussed in this paper will upgrade future research and engineering works on IDPSs for IoT. [ABSTRACT FROM AUTHOR]