Securing IoT Devices in Smart Homes Using ML-Based Anomaly Detection

Smart homes are rapidly adopting Internet of Things (IoT) devices, which presents substantial security issues due to the growing attack surface of possible cyber attacks caused by the increasing number of networked devices. These risks are beyond the reach of traditional security methods, including signature-based intrusion detection systems, especially in IoT contexts with limited resources. To improve the security of IoT devices in smart homes, this study suggests an anomaly detection system based on machine learning (ML). The suggested approach enables real-time detection of known and unknown assaults by utilizing both supervised and unsupervised machine learning techniques to spot irregularities in device behavior. Our solution minimizes processing cost and reduces false positives, therefore addressing the shortcomings of traditional approaches. The study also explores the challenges posed by data privacy, latency, and the high volume of unlabeled data, proposing solutions through lightweight ML models suited for IoT architectures. Experimental results demonstrate the effectiveness of our system in improving the detection of cyberattacks, while maintaining efficient performance in resourceconstrained environments.