Abstract

IoT Security Enhancement Using Machine Learning for Real-Time Threat Detection. The Internet of Things (IoT) is a network of interconnected devices that communicate over the internet, offering convenience and efficiency but also posing significant security risks. This project focuses on enhancing IoT security by detecting cyber threats such as DDoS, DoS, reconnaissance, and data theft in real-time. The main objective is to improve the accuracy and efficiency of threat detection using machine learning models like Random Forest. The system analyzes key network behaviors, including data flow, traffic anomalies, and device interaction patterns. Unlike traditional rule-based intrusion detection systems, this approach dynamically monitors network activities to identify potential threats. The system has wide applications across domains such as smart homes, industrial IoT, healthcare, and autonomous systems, contributing to a more secure and resilient IoT ecosystem.