A Review on Machine Learning Techniques for Secure IOT Networks

The Internet of Things (IoT) is a game-changing development of the Fourth Industrial Revolution because it paves the way for previously inconceivable levels of connectivity and collaboration amongst many types of intelligent, autonomous things. The impact of the Internet of Things on our professional, financial, and social lives seems to be considerable. The Internet of Things is allowing “smart” buildings and “smart” people to give way to “smart” communities and even “smart” planets. Yet, the massive number of interconnected devices poses a challenge to IoT network security because of the many protocols they use. The inability to improve the security of IoT devices is related to their limited computing power. Thus, novel techniques are required to safeguard IoT networks. In the field of security research, ML models have been in the forefront for the last several years. Machine learning (ML) algorithms may provide a practical solution for securing IoT infrastructure considering the massive amounts of data created daily by IoT devices. The major goal of this study is to provide a thorough review of the many studies that have been conducted about ML deployment and IoT security. We briefly covered how security holes in IoT networks might compromise the confidentiality of private data. The advantages, disadvantages, and applications of various ML methods and models on the Internet of Things were then explained. Existing IoT networks’ use of machine learning models for user security is also examined. The challenges of using ML models to secure IoT networks were also covered, since they may suggest new research directions.