A Comprehensive Review of IoT Network Security using Machine Learning Techniques
Pooja K. Shah, Amita V. Shah., Hetal Bhargav Pandya · 2025
The transformation of various industries due to the Internet of Things (IoT) brings various devices, data, and processes together in divergent ways. The IoT has serious challenges that should be resolved in spite of its positive effect on the world. A low-resource device is often implemented in unsafe configurations, thereby making them more susceptible to different threats. The IoT has the capacity to optimize many spheres of everyday life by facilitating more interaction, integrating technologies, and improving decision-making based on data. Nevertheless, the prevalence of IoT devices in use has also increased the chances of network intrusions and other security complications. Although the IoT is promising in the fields of home automation, healthcare, and industry, the usage of open-source infrastructure, insufficient software update, and simple security safeguards provide cyberattacks opportunities to gain access to critical data and services and remain appealing targets. Complexity in cyberattacks is also increasing to pose even greater threats to both public and privately-owned organizations. This review paper focuses on the security of IoT network, and the role and importance of security in IoT network, and the use of machine learning in anomaly problem identification as means of strengthening the security of IoT network. It further gives a comparative analysis of the research objectives, data sets used, techniques and algorithms, the limitations identified and the future scope and difficulties and challenges. The review is based on insights and results of 20 chosen papers, which provide an overall understanding of techniques applied in this area.