International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12

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 Volume 12, Issue 12 (December 2025), Pages: 100-112

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 Original Research Paper

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

 Author(s): 

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

 Affiliation(s):

  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

 Full text

    Full Text - PDF

 * Corresponding Author. 

   Corresponding author's ORCID profile:  https://orcid.org/0000-0003-3770-432X

 Digital Object Identifier (DOI)

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

 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.

 © 2025 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

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

 Article history

 Received 1 July 2025, Received in revised form 4 November 2025, Accepted 19 November 2025

 Acknowledgment

No Acknowledgment. 

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Khayyat MM, Tashkandi A, Qashlan A, Gashgari GA, Khayyat MM, and Gupta SK (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

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 Figures

  Fig. 1  Fig. 2  Fig. 3  Fig. 4  Fig. 5  Fig. 6  Fig. 7  Fig. 8 

 Tables

  Table 1  Table 2  Table 3  Table 4 

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