Citation :

Prasanta Pratim Bairagi, Ashish Bagwari, Sailen Dutta Kalita, Latika Deka, Jyotshana Bagwari, Ciro Rodriguez, Yuri-Arturo Pomachagua-Basualdo, Glissett-Jansey Mendoza-Gastelo, "Adaptive Machine Learning Framework for Real-Time Cyber-Attack Detection and Prevention in IoT Networks," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 7, pp. 23-33, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I7P103

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.

Keywords

Cybersecurity, Cyber Attacks, Internet of Things (IoT), Intrusion Detection Systems (IDS), Machine Learning (ML), Network Security.

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