Investigations on IoT Security System using Machine Learning Algorithm

The term “Internet of Things” (IoT) is used to describe the expanding network of networked computing and software systems. Long periods of time in Internet of Things which faults go unreported are bad for users, raise the risk of cyberattacks and identity theft, drive up costs, and reduce earnings. Intruder detection systems are widely used to prevent malicious activity on computer networks (IDS). To evaluate the efficacy of IoT IDS, several studies have employed various deep learning and machine learning algorithms on different datasets. We propose a model using machine learning techniques, specifically K-Nearest Neighbors, Logistic Regression and Bayesian networks, to detect the greatest number of attacks in IoT networks and to recognize real global intruders. The dataset is most likely one of the most important starting points for using those techniques. The goal of this analysis is to shed light on the need of using ML-based methodologies to effectively handle IoT security in a way that is genuinely effective, adaptable, and seamless.