AI-Based Intrusion Detection and Prevention System

The explosive expansion of smart home IoT devices is the main reason why cybersecurity has become a very sensitive issue. This paper presents an Artificial Intelligence-based Intrusion Detection and Prevention System (IDPS) that is a solution for smart homes. The system achieves accurate threat recognition by adopting machine learning and deep learning techniques, and it also limits false alarm rates via adjustable security policies. It utilizes both signature-based and anomaly-based methods to detect attacks that have been previously recorded as well as newly developed ones. Nonetheless, it is facing problems such as a heavy computational load, privacy issues, and susceptibility to attacks. Research in this field may be directed towards the development of energy-efficient models and scalability improvement for huge IoT networks. Besides that, the integration of instant monitoring, self-regulating algorithms, and blockchain-enabled sharing of threat intelligence can elevate security levels. In this way, the development of a robust, intelligent, and dependable smart home IoT environment will be facilitated.