A Comprehensive Analysis of Machine Learning and Deep Learning Approaches towards IoT Security

Through the IoT, millions of smart devices may be networked together and set to automatically exchange information with one another. By 2022, the number of connected devices is projected to have reached 80 billion, making IoT one of the fastest-growing digital sectors. Furthermore, additional security concerns have arisen due to the interconnectedness of IoT devices and the many different fields that must work together to install such devices. The underlying weaknesses of IoT devices render useless the implementation of security mechanisms such as protection from cyber threats, authorization, access control, networking, and information protection. To protect the IoT ecosystem, it is necessary to improve upon current security checks. As a result of recent developments in Machine Learning and Deep Learning (ML-DL), artificial intelligence has moved from the realm of science fiction to that of everyday life in several crucial contexts. Therefore, ML-DL techniques play a crucial role in advancing the protection of IoT systems beyond just ensuring encrypted communications between IoT components. To better secure IoT devices, this analysis helps to give a complete overview of ML strategies and current breakthroughs in DL strategies. Vulnerabilities to the protection of the IoT are addressed, including those that have always been present as well as those that are relatively new. The merits, demerits, and overall viability of the ML/DL approach to IoT protection are then described and analyzed. The use of ML/DL to enhance IoT security is explored, along with its advantages and disadvantages. These prospects and difficulties suggest avenues for future study.