Abstract: Advanced threat detection systems are needed more than ever as cyber-attacks become more advanced. A novel cybersecurity model uses Bipolar Fuzzy Rough Sets, Graph Neural Networks, and dense network (BFRGD-Net) architectures to identify threats with unmatched accuracy and speed. The approach optimizes threat detection using Dynamic Range Realignment, anomaly-driven feature enhancement, and a hybrid feature selection strategy on a comprehensive Texas dataset of 66 months of real-world network activity. With 97.8% accuracy, 97.5% F1-score, and 98.3% AUC, BFRGD-Net sets new standards in the field. Threat Detection Sensitivity shows the model’s capacity to find uncommon, high-severity threats, while Balanced Risk Detection Efficiency provides fast, accurate threat detection. The model has strong correlations and the highest statistical metrics scores compared to other techniques. Extensive simulations demonstrate the model’s capacity to discern threat levels, attack kinds, and response techniques. BFRGD-Net revolutionizes cybersecurity by seamlessly merging cutting-edge machine learning with specific insights. Its advanced threat detection and classification engine reduces false negatives and enables proactive critical infrastructure protection in real-time. The model’s adaptability to various attack situations makes it vital for cybersecurity resilience in a digital environment.

Qian Zhang, “Optimizing Threat Intelligence Strategies for Cybersecurity Awareness Using MADM and Hybrid GraphNet-Bipolar Fuzzy Rough Sets” International Journal of Advanced Computer Science and Applications(IJACSA), 15(11), 2024. http://dx.doi.org/10.14569/IJACSA.2024.01511136

@article{Zhang2024,
title = {Optimizing Threat Intelligence Strategies for Cybersecurity Awareness Using MADM and Hybrid GraphNet-Bipolar Fuzzy Rough Sets},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01511136},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01511136},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {11},
author = {Qian Zhang}
}