Adaptive Machine Learning Framework for Real-Time Cyber-Attack Detection and Prevention in IoT Networks

Abstract

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.