Behavioral Analysis for Complex/ Dense IoT Network for Authenticated Sensing Results Using Machine Learning

Abstract

The rapid advancement of technology has propelled the Internet of Things (IoT) to new heights, demanding a strong focus on device security. Machine Learning (ML) algorithms play a vital role in collecting and analyzing data to identify patterns. The IoT originated from Software Defined Networks (SDN), but the infiltration of malicious data poses a threat. The concept of the Internet of Behaviors (IoB) uses intelligent algorithms to address these challenges. Efficiency is crucial in IoT networks, where battery-powered nodes collect and distribute information selectively. Confidentiality, integrity, and availability are essential aspects of network security, protected by measures like access control, authentication, and encryption. Behavior-based frameworks detect breaches and offer solutions. This research combines advanced ML algorithms, a multifaceted security system, and numerical analysis using MATLAB and Python. The results demonstrate improved accuracy in IoT network throughput and enhanced quality activation, considering networks with static, dynamic, and secured nodes.