Role of machine learning in IoT security

The Internet of Things (IoT) is a broadening technology that connects and combines billions of smart objects, producing immense amounts of data and influencing many areas of everyday life and business operations. On the other hand, the intrinsic attributes of IoT, such as their short life of batteries, widespread connection, limited resources design, and mobility, cause them to be especially susceptible to cybersecurity threats, that are growing rapidly. Therefore, there has been considerable research interest in the field of IoT security and privacy, specifically in advancing anomaly detection systems. Machine learning (ML) has had significant advancements in recent years, growing up from an original idea in research labs to a robust tool in essential industries. Imagine a giant network of everyday objects with built-in sensors or controls. These devices talk to each other using wires or radio signals, creating a vast interconnected system. IOT is revolutionizing how technology seamlessly combines into our everyday lives. Its reach extends to key areas like healthcare industries, intelligent homes, and intelligent cities. The burgeoning number of IoT devices and applications, however, has given rise to security and privacy concerns. As IoT devices become more common, security risks grow. These include unauthorized data breaches, impersonation of legitimate devices, and various cyberattacks like DoS assaults, data interception, and unauthorized system access (intrusion detection. Developments in deep learning (DL) and machine learning (ML) present viable answers to the mounting security issues affecting IoT devices. One promising approach to address security and privacy issues in the Internet of Things (IoT) is machine learning. This work is a study that analyzes the current security and privacy issues in the Internet of Things (IoT) surroundings. The most recent machine learning-based models and approaches to address these issues are then presented, and their operation is examined. The neural network was the most successful model, according to my study. KEYWORDS: Attacks, Anomaly Detection, Cybersecurity, Internet of Things (IoT), Machine Learning (ML)