Developing Smart IoT Security Frameworks through Machine Learning

Abstract

The exponential growth of IoT devices has introduced unprecedented security challenges across healthcare, smart cities, and industrial domains. Traditional security mechanisms prove inadequate against sophisticated cyber threats targeting resource-constrained IoT environments. This research presents a comprehensive framework integrating machine learning techniques with smart IoT security architectures for enhanced threat detection and adaptive defense mechanisms. The study evaluates supervised, unsupervised, and deep learning approaches using contemporary datasets (CICDDoS2019, BoT-IoT, UNSW-NB15). The proposed framework achieves superior performance with accuracy rates exceeding 98.5% for ensemble models and 97.8% for deep learning implementations. Key findings reveal that CNN architectures achieve optimal balance between detection accuracy and computational efficiency. The framework incorporates edge computing for reduced latency and real-time processing. Results demonstrate significant improvements in threat mitigation, scalability, and adaptability to emerging attack vectors. This research contributes to advancing IoT security through intelligent, adaptive frameworks capable of autonomous threat management while maintaining system performance and user privacy.