In the rapidly evolving landscape of the Internet of Things (IoT), the proliferation of devices has introduced significant security vulnerabilities, exposing networks and data to potential intrusions and attacks. This research is motivated by the critical need to enhance the security of IoT ecosystems through effective detection of these threats. We propose a novel intrusion detection system (IDS) that leverages machine learning (ML) techniques to identify and mitigate security threats in IoT networks. Our methodology involves the design and implementation of a ML-based IDS framework, which includes data preprocessing, feature extraction, and the deployment of multiple ML algorithms to detect anomalous behavior indicative of security threats. We evaluated our system using a comprehensive dataset comprising both normal IoT traffic and a range of simulated attack scenarios. The performance of our IDS was benchmarked against several metrics, including accuracy, precision, recall, and F1 score. Our results demonstrate that the ML-based IDS significantly outperforms traditional IDS solutions in detecting a wide variety of attacks, with notable improvements in detection rates and reduced false positives. The significance of our research lies in its contribution to the field of IoT security, providing a robust and scalable solution that can adapt to the evolving nature of threats. By integrating advanced ML techniques, our system offers enhanced detection capabilities, thereby improving the overall security posture of IoT networks. This work not only addresses current security challenges but also lays the groundwork for future research in the area of intelligent and adaptive security solutions for the Internet of Things.

Internet of Things (IoT), Intrusion Detection System (IDS), Machine Learning (ML), IoT Security, Anomaly Detection, Feature Extraction, Dataset, Attack Scenarios, Performance Metrics, Security Vulnerabilities,