Artificial Intelligence Intrusion Detection Framework for Smart Home IoT Using Machine Learning

There is a chance of hacking, illegal access, and data breaches for anyone who uses smart home IoT devices. Since conventional security measures no longer cut it, AI will have to detect attacks as they happen and adapt defences accordingly. Using real-time threat and anomaly detection, our machine-learning-based smart-home IoT security keeps our family safe. We use deep learning, supervised learning, and unsupervised learning models to assess threats, malicious behaviour, and network traffic. Compared to autoencoders and isolation forests, CNNs and random forests are better at detecting suspicious device activity. Smart home systems can withstand attacks thanks to dynamic security rules enhanced through reinforcement learning. The proposed ML-based security framework outperforms rule-based systems on real-world smart-home datasets in detection accuracy, false-positive rates, and reaction times. The results demonstrate that to protect smart home IoT infrastructure from today’s risks, AI-driven security solutions are required. Upcoming innovations include explainable AI for threat detection, edge security for real-time processing, and interoperability across the Internet of Things (IoT) ecosystem. By researching security and threat detection, we educate capable individuals to become security managers on their own. This enhances consumer security in an interconnected environment and deactivates smart homes.