A comprehensive analysis of hybrid machine learning algorithms for securing IoT data

With a system of various interconnected devices, IoT has revolutionized the application of different applications. Since IoT deals with heterogeneous devices, it becomes easy for the attacks to launch cyber-attacks. In addition, the resource constraint and distributed nature of the IoT network increases the challenges related to security and privacy of IoT devices. Various attack detection and security techniques have been proposed in existing literary works. This paper provides a comprehensive analysis of machine learning (ML) algorithms for securing IoT data. The review will emphasize the analysis and comparison of different ensemble methods and feature extraction algorithms for enhancing IoT security. In addition, this review will analyze the challenges and open issues based on the existing literary works.