International Journal of Multidisciplinary Research and Growth Evaluation  |  ISSN (Online): 2582-7138  |  Double-Blind Peer Review  |  Open Access  |  CC BY 4.0

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     2026:7/3

International Journal of Multidisciplinary Research and Growth Evaluation

ISSN (Online): 2582-7138 | Open Access

AI-Driven Supply Chain Threat Intelligence: Real-Time Detection of Cyber Attacks on Manufacturing and Logistics Networks

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Volume: 4 | Issue: 6 | Pages: 1411-1417

Subject: Management

Country: United States

DOI: https://doi.org/10.54660/IJMRGE.2023.4.6.1411-1417

License: CC BY 4.0

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Abstract

The rapid digital transformation of manufacturing and logistics sectors has created unprecedented interconnectivity across global supply chains, simultaneously exposing these critical infrastructures to sophisticated cyber threats. Traditional security approaches relying on signature-based detection and rule-based methods have proven inadequate against the evolving landscape of advanced persistent threats, ransomware campaigns, and state-sponsored attacks targeting operational technology environments. This paper examines how artificial intelligence-driven threat intelligence frameworks can enable real-time detection and situational awareness of cyber-attacks in manufacturing and logistics ecosystems. Through comprehensive analysis of current threat landscapes, machine learning methodologies, and operational deployment considerations, this study presents a structured framework for integrating AI capabilities across supply chain networks. The research demonstrates that AI-enhanced detection systems, incorporating anomaly detection algorithms, behavioral analysis, and predictive threat identification, can achieve detection accuracies exceeding 90% while significantly reducing mean time to detection. The findings underscore the critical importance of multi-layered AI integration spanning network telemetry analysis, operational technology sensor monitoring, and cross-organizational threat correlation for securing modern supply chain infrastructures against an increasingly hostile cyber environment.

How to Cite This Article

Omowunmi Folashayo Makinde, Nathaniel Adeniyi Akande, Udoka Cynthia Duruemeruo, Uju Judith Eziokwu, Olatunde Ayomide Olasehan (2023). AI-Driven Supply Chain Threat Intelligence: Real-Time Detection of Cyber Attacks on Manufacturing and Logistics Networks . International Journal of Multidisciplinary Research and Growth Evaluation (IJMRGE), 4(6), 1411-1417. DOI: https://doi.org/10.54660/IJMRGE.2023.4.6.1411-1417

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Publication Information

Journal: International Journal of Multidisciplinary Research and Growth Evaluation (IJMRGE)

Publisher: Anfo Publication House

ISSN: 2582-7138 (Online)

Frequency: Bimonthly

Language: English

Open Access: Yes - This article is distributed under the terms of the Creative Commons Attribution 4.0 International License

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