Abstract: This paper introduces a fuzzy decision support system (FDSS) based on a graph neural network (GNN) for anomaly detection and intelligent security. The primary aim is to develop a robust system capable of accurately identifying anomalies and providing timely incident response services. GNNs are utilized to capture the complex relationships and features between nodes in graph data, learning the embedded representation of each node through information transfer and aggregation mechanisms, which encapsulate the structural information of the graph. The FDSS leverages these features to construct a fuzzy rule base and perform fuzzy inference, generating decision suggestions that enhance the system's adaptability and robustness in dealing with uncertain data. The challenges addressed include the need for efficient anomaly detection in large-scale surveillance networks, the requirement for fast response times during emergencies, and the necessity for scalable and adaptable systems. Experimental results demonstrate that the GNN-based FDSS surpasses other methods in terms of anomaly detection accuracy, incident response service efficiency, system processing capacity, and model generalization ability. Compared to traditional statistical methods, machine learning models, and deep learning models, the proposed system maintains high precision and recall rates, processes data more efficiently, and adapts well to new datasets.

Tao Chen and Xiaoqian Wu, “Application of Fuzzy Decision Support System Based on GNN in Anomaly Detection and Incident Response Service of Intelligent Security” International Journal of Advanced Computer Science and Applications(IJACSA), 15(9), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150912

@article{Chen2024,
title = {Application of Fuzzy Decision Support System Based on GNN in Anomaly Detection and Incident Response Service of Intelligent Security},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150912},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150912},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {9},
author = {Tao Chen and Xiaoqian Wu}
}