Adaptive Machine Learning Framework for Real-Time Cyber-Attack Detection and Prevention in IoT Networks
This paper introduces an innovative ML-based security paradigm that improves the attack detection accuracy by combining adaptive feature extraction techniques with a context-attentive hybrid mechanism and maximizes detection accuracy and computational efficiency.