A Machine Learning-Based Methodology for IoT Security

The popularity of IoT and gadget connectivity is rapidly growing in the present world. Cyber risk has become a significant issue for IoT devices, particularly edge devices, as D2D communication and Internet traffic have grown. The use case of machine learning for IoT security is presented in this proposal. Recent scientific advancements often lead to the development of new technologies. In order to help with this proposal, machine learning is now used by our society. It is because maintaining and managing the data is extremely difficult for humans. As new technologies are developed, cybersecurity measures can be enhanced through innovative methods. The most recent and promising strategy in cyber-physical security is machine learning (ML), which can help address a number of burgeoning issues. Explore potential problems with IoT security controls when applying machine learning to IoT systems and the design of IoT systems. Because smart gadgets are so accessible and in such high demand, IoT systems are exposed to new cyber-physical security and privacy assaults. IoT systems need to be secured with strong, adaptable, and contemporary security techniques.