Abstract: IoT devices seem like easy targets to attackers because manufacturers limit their computing capability and maintain insufficient security defenses. The present paper provides extensive analysis about machine learning techniques that enhance the security of IoT networks. ML operates in a dual capacity where systems need specific design to maintain defense security together with protection against potential attacks. The research evaluates multiple machine learning models active in real-time intrusion detection systems and explains their weak points along with present-day IoT cybersecurity threats analysis. The study describes vital barriers alongside anticipated advancements that will lead to the development of secure intelligent IoT networks.
Keywords: IoT, Machine Learning, Cybersecurity, Anomaly Detection, Threat Prediction, Supervised Learning, Unsupervised Learning, Adversarial Attacks
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DOI:
10.17148/IJARCCE.2025.14476
