Real-Time Cybersecurity Monitoring for Smart Homes with AI

Abstract

The rapid growth of state-of-the-art housing technologies has revolutionized modern life by automation and monitoring household appliances. However, increasing connectivity and integration of internet devices pose challenges. The report provides a comprehensive framework for real-time cyber security surveillance using artificial intelligence to identify and control threats in the Smart Home environment. The proposed structure uses deep learning methods to analyses network traffic and identify possible penetration. Using machine learning models such as computational learning models (CNS) and repeat neurological networks (RNNS), the computer reaches higher accuracy in detecting cyber threats by minimizing the wrong passivity. In addition, we introduce an adaptive insight that continue to improve its knowledge base to respond to the formation of attack vectors. Test results prove that the surveillance system run by AI significantly improves the ability to detect the threat compared to traditional signature approaches. This research highlights the potential of Artificial Intelligence. This will help provide immediate security solutions for smart home vacations and promote safe and flexible digital environments.