Securing IoT Networks Using Machine Learning, Deep Learning Solutions: A Review

The Internet of Things (IoT) is the next big thing not only in our personal lives, but also in the commercial, economic, and social aspects of the world. IoT networks usually have resource-limiting nodes due to which they become easy targets for online attacks. Data scientists and experts are looking for ways to make extensive solutions to meet privacy and security needs in IoT. However, traditional approaches are not sufficient to monitor the whole network as it is very complex and requires great computation power. This way, there is a strong need for advanced Deep Learning and Machine Learning solutions to make IoT devices robust with embedded intelligence. IoT applications have come a long way over the years, but they still need to develop further. In this paper, we deeply discuss and review the existing security requirements and solutions to deal with various problems for IoT networks. The comparative analysis of security solutions based on machine learning and deep learning techniques is discussed. We also review the complexity and distinctiveness of IoT and its security measures. Despite having a lot of advanced solutions, peculiarities of IoT are still prevalent, such as resource-limiting devices with limited energy, memory, and computational power. These issues are still a barrier to wide-scale adoption. Future work is still needed to deal with the limitations of IoT and ML and DL methods. This study might serve as a research path for future works in this direction.