Authors - Suganya Moorthy, Jayakumar Kaliappan Abstract - Internet of Things (IoT) networks have grown really fast, which has increased the attack surface of cyber attacks by a big mar gin. However, the severely limited computational resources, the hetero geneous architecture, and incomplete or decentralized communications make the IoT environments very susceptible to intrusion attacks, in cluding Distributed Denial of Service (DDoS), spoofing, botnets, and data exfiltration attacks. Older signature-based intrusion detection sys tem (IDS) is not effective in detecting zero-day and dynamic threats. The paper will present a new machine learning-based intrusion detection system, which was developed with IoT networks in mind. The design proposed combines the characteristics of feature search, feature detec tion, and group classification model in order to increase the accuracy of detection as well as reduce the number of computations. Benchmark IoT intrusion datasets that have undergone experimental evaluations prove to be more effective in detection accuracy, false positive rates and scaling than the traditional IDS frameworks. Practical constraints that include the computational overhead of resource-constrained IoT devices, imbal ance of the dataset, and interpretability of the model are addressed. The directions of future research are lightweight federated learning systems, explainable AI system incorporations, and real-time adaptive threat in telligence systems to build better resiliencies of IoT security.
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