Machine Learning Approaches to Securing IoT Networks: A Comprehensive Review of Recent Advances
Harnit Saini, Sanjeev Kumar Prasad · 2024
The IoT is a network of physical objects embedded with sensors and software, connecting and exchanging data over the internet. With over 7 billion connected devices, experts predict growth to 10 billion by 2020 and 22 billion by 2025. Machine learning ML technologies are advancing quickly, with new algorithms and approaches emerging regularly. In this paper, a review is carried out that helps provide insights into their effectiveness in the current landscape. Different ML techniques are categorized and evaluated based on how they address diverse challenges. Reviewing current ML techniques helps in understanding their strengths and limitations in combating novel attack vectors. Later on, a comparative analysis of these techniques is conducted based on performance metrics. A comprehensive review identifies gaps in current research and practice, highlighting areas where ML techniques might need further development or improvement.