International Research Journal of Education and Technology

Smart home security has become an essential component of modern living, incorporating artificial intelligence (AI) and machine learning (ML) to enhance safety and privacy. As the number of smart home devices increases, so do the risks of cyber threats, unauthorized access, and physical intrusions. Traditional security systems, such as alarm-based setups and manually monitored surveillance cameras, often lack efficiency and adaptability. They rely on predefined rules and static configurations, which can result in frequent false alarms or missed security breaches. Machine learning introduces a more dynamic approach by enabling systems to learn from historical data, recognize patterns, and make intelligent security decisions in real time. Anomaly detection models analyze behavioral patterns of home occupants and identify suspicious activities. Facial recognition and biometric authentication powered by deep learning enhance user verification, while AI-driven predictive analytics help preempt potential threats before they occur. Furthermore, ML-powered smart security systems integrate with the Internet of Things (IoT) to provide seamless communication between devices, ensuring a coordinated security response. This paper delves into different ML techniques used in smart home security, including supervised and unsupervised learning, deep learning, and reinforcement learning. It also explores the role of Python and its libraries in building security models, such as TensorFlow, OpenCV, and Scikit-Learn. Additionally, it discusses the challenges associated with implementing ML-based security solutions, such as data privacy concerns, adversarial attacks, and system scalability. Ethical considerations, including bias in AI-driven security decisions, regulatory compliance, and user data protection, are also examined. Finally, the paper highlights future advancements in AI-driven home security, such as edge computing for faster local threat detection, federated learning to enhance privacy, and AI-powered autonomous security drones. As smart homes continue to evolve, integrating machine learning into security systems will be vital for providing adaptive, intelligent, and proactive protection against an ever- changing threat landscape.

Keywords: Smart security, Machine Learning, Deep Learning, Python, Medical Imaging, AI in Healthcare, Ophthalmology.