Adaptive Neuro-Fuzzy Methodology for the Information Security Risk Assessment in Transport-Logistics Centers
DOI:
https://doi.org/10.47839/ijc.25.2.4661Keywords:
Information Security, Adaptive Neuro-Fuzzy Methodology, Risk Assessment, Logistics, ManagementAbstract
This paper presents an adaptive neuro‑fuzzy methodology for integrated information security risk assessment in transport and logistics centers (TLCs). The proposed approach is based on a unified mathematical model for aggregating factor groups and is implemented through a comprehensive conceptual model that combines the transparency of expert knowledge with the adaptability of machine learning. The model includes six interconnected components that support a complete analytical cycle from data acquisition to managerial decision‑making. A key element, the Mamdani Rule Base Generator, automates the creation of the expert rule base, reducing time costs and minimizing expert involvement. To address the computational complexity of generating large rule sets and to mitigate the “cold start” problem caused by limited historical data, the system incorporates a Rete algorithm. Adaptive Neuro‑Fuzzy Inference (ANFIS) and a second‑order Takagi-Sugeno-Kang (TSK) network enable nonlinear modeling and accurate approximation of expert knowledge. Experimental validation through the “Dynamic Adaptation” test confirmed the system’s ability to automatically reconfigure risk assessment logic under changing external conditions. The results demonstrate robustness, scalability, and practical applicability for proactive information security management in TLCs. The methodology was validated using a real-world operational dataset across three transport and logistics centers. The neuro-fuzzy model achieved an overall classification accuracy of 95.2%, demonstrating high selectivity and stability in identifying critical information security states.
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