LGPR Publication Success in International Journal of Advanced and Applied Sciences by Dr. Mashael M. Khayyat

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Dr. Mashael M. Khayyat, LGPR Candidate published in the Journal of Advanced and Applied sciences.

Journal of Advanced and Applied sciences, 12, 12 (December 2025), Pages: 100-112 |

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Cite Score: 1.0

International Journal of ADVANCED AND APPLIED SCIENCES (IJAAS), a scientific broadcasting organization and media, provides an international medium for the communication of original research, ideas and developments in all areas of the field of Applied Sciences. IJAAS tries to apply existing scientific knowledge to develop more practical applications like technology or inventions to solve immediate, real-life problems in a scientific manner. Its main scope is application of scientific knowledge transferred into a physical environment which embraces branches of applied science such as: Engineering, Applied mathematics, Applied physics, Medicine, and Computer science.

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

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

Mashael M. Khayyat

Lincoln University College, Petaling Jaya, Malaysia.

Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia

Araek Tashkandi

Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia.

Amjad Qashlan

Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia.

Ghada A. Gashgari

Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia.

Manal M. Khayyat

Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah, Saudi Arabia.

Shashi Kant Gupta

Lincoln University College, Petaling Jaya 47810, Malaysia.

Center for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab 140401, India.

Abstract

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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.

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Flow chart of the Framework