Developing an intelligent model for accurate detection of cyber threats in smart logistics networks

Authors: Mashael M. Khayyat ^{1, 2,} *, Araek Tashkandi ², Amjad Qashlan ³, Ghada A. Gashgari ³, Manal M. Khayyat ⁴, Shashi Kant Gupta ^{1, 5}

Affiliations:

¹Lincoln University College, Petaling Jaya, Malaysia
²Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
³Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
⁴Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah, Saudi Arabia
⁵Center for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab 140401, India

Abstract

Supply Chain Management in the logistics sector involves coordinating processes, resources, and information to ensure the smooth flow of goods and services from suppliers to end customers. However, smart logistics networks are increasingly exposed to cyber threats such as data breaches, ransomware, and unauthorized access to IoT devices, which can disrupt operations and compromise sensitive data. In this study, the BoT-IoT dataset from the Kaggle platform is used. Data preprocessing is performed using Z-score normalization to standardize the data. Principal Component Analysis (PCA) is applied to reduce dimensionality, while Recursive Feature Elimination (RFE) is used to select the most relevant features. For classification, a novel Optimized Grey Recurrence Neuro Net Classifier is developed, which combines the global search capability of the Grey Wolf Optimizer (GWO) with Recurrent Neural Networks (RNNs) to improve detection performance. The model is implemented using Python tools and libraries. Experimental results show that the proposed method outperforms existing approaches, achieving 99.99% accuracy, 99.99% precision, 100% recall, and a 99.99% F1 score, demonstrating its high effectiveness and efficiency.

Keywords

Supply chain management, Smart logistics, Cyber threats, Feature selection, Grey recurrence neuro net

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DOI

https://doi.org/10.21833/ijaas.2025.12.010

Citation (APA)

Khayyat, M. M., Tashkandi, A., Qashlan, A., Gashgari, G. A., Khayyat, M. M., & Gupta, S. K. (2025). Developing an intelligent model for accurate detection of cyber threats in smart logistics networks. International Journal of Advanced and Applied Sciences, 12(12), 100–112. https://doi.org/10.21833/ijaas.2025.12.010