Safeguarding IOT Networks Using Machine Learning For Intrusion Detection & Prevention

The exponential growth of Internet of Things (IoT) devices has introduced unprecedented connectivity, but it has also significantly expanded the cyber threat landscape. Traditional security mechanisms often fail to adapt to the dynamic and heterogeneous nature of IoT environments. This research proposes a robust, machine learning (ML)-driven framework for intrusion detection and prevention tailored specifically to safeguard IoT networks. The framework incorporates both supervised and unsupervised learning models to detect anomalous behavior and known attack patterns in real time. By leveraging continuous monitoring, adaptive learning algorithms, and intelligent data preprocessing, the system ensures minimal false positives and high detection accuracy across varied IoT deployments. The study further explores the integration of edge and fog computing to reduce latency and improve decision-making efficiency. Comprehensive evaluations using benchmark datasets demonstrate the system’s ability to identify complex intrusion vectors such as DDoS, spoofing, and data exfiltration. The findings highlight the potential of ML in establishing a resilient and scalable security architecture for future-proofing IoT networks. This thesis contributes both a conceptual model and practical implementation guidelines, ultimately enhancing trust, reliability, and intelligence in connected environments.