A REVIEW ON SECURE MACHINE-TO-MACHINE COMMUNICATION IN INDUSTRIAL IOT: CHALLENGES AND COMPUTATIONAL INTELLIGENCE APPROACHES

Remya K; Joshna M

doi:10.29121/shodhai.v3.i1.2026.102

Authors

Remya K Department of Computer Science, R S M SNDP Yogam College of Arts and Science, Government Aided College, Affiliated to the University of Calicut, Koyilandy, Kerala, India
Joshna M Department of Computer Science, R S M SNDP Yogam College of Arts and Science, Government Aided College, Affiliated to the University of Calicut, Koyilandy, Kerala, India

DOI:

https://doi.org/10.29121/shodhai.v3.i1.2026.102

Keywords:

Industrial Internet Of Things, Machine-To-Machine Communication, Cybersecurity, Deep Learning, Blockchain, Fog Computing

Abstract

The Industrial Internet of Things (IIoT) revolutionizes manufacturing and industrial processes by enabling seamless Machine-to-Machine (M2M) communication. However, this interconnectivity introduces critical security vulnerabilities that can compromise operational technology (OT) systems, leading to catastrophic failures. This review paper comprehensively analyzes the security challenges inherent in IIoT M2M communication, focusing on threats like data breaches, unauthorized access, and denial-of-service attacks. It surveys traditional cryptographic solutions and highlights their limitations in resource-constrained IIoT environments. The paper then explores the transformative potential of computational intelligence (CI) techniques, including deep learning, lightweight machine learning, and bio-inspired algorithms, for anomaly detection, intrusion prevention, and adaptive security. By synthesizing findings from recent literature (2019-2024), this review identifies key research trends, such as the shift from cloud-centric to edge-based security models and the integration of blockchain for decentralized trust. The analysis concludes that hybrid models combining lightweight CI algorithms with fog/edge computing architectures represent the most promising direction for developing robust, efficient, and scalable security frameworks for future IoT systems.

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