Adaptive Machine Learning Framework for Real-Time Cyber-Attack Detection and Prevention in IoT Networks
Critical vulnerabilities have been made public by the fast expansion of Internet of Things (IoT) devices, making these networks easy target for cyber-attacks. While security solutions based on Machine Learning (ML) have shown potential, they often encounter issues including slow detection times, scaling issues, and a lack of generalisability when it comes to diverse IoT devices. To work with these issues, this paper introduces an innovative ML-based security paradigm. The proposed framework improves the attack detection accuracy by combining adaptive feature extraction techniques with a context-attentive hybrid mechanism. The new paradigm maximizes detection accuracy and computational efficiency. This is achieved through real-time dynamic adjustment of feature selection against network conditions, rather than traditional hybrid approaches. Furthermore, a lightweight and scalable detection method fit for execution on low-resource IoT devices is offered. It is apt for several IoT environments. The proposed framework beats several current models by 15% in accuracy, 25% in the reduction of false positive rates, and 30% in detection times, according to experimental tests carried out on numerous IoT datasets.