Enhancing Cybersecurity in the Big Data Era: A GA-Optimized Fuzzy Clustering Approach
Tieguang Xu, Can Ma, Zhuo Su, Jingqiong Su, Zhiming Ma, Jianzhen Wang, Zhaolong Yang · International Journal of Uncertainty Fuzziness and Knowledge-Based Systems · 2025
Research Highlights • This study proposes a novel approach for enhancing cybersecurity through the integration of GA-AFCM. This method significantly improves the accuracy, efficiency, and adaptability of IDS. • The GA-AFCM technique demonstrates superior performance compared to conventional methods such as K-Means, MKKM-IC, Density Peaks, and GMM. • The proposed method effectively addresses the challenges of security of information in the period of Big Data, achieving the highest detection rate while significantly reducing false positives, thereby enhancing overall system reliability and efficiency. Cybersecurity includes protecting computer networks and systems from unauthorized access, harm, and fraud, employing various techniques and technologies such as barriers, antivirus software, and cryptography. Regular system updates, employee training, and adherence to best practices are crucial for maintaining confidentiality and ensuring reliable IT services in both corporate and public sectors. This paper introduces a GA-AFCM technique, which enhances intrusion detection and cybersecurity tasks by combining the strengths of Genetic Algorithms and Adaptive Fuzzy C-Means Clustering. The study began with data collection and preprocessing using Z-score normalization, followed by feature extraction through Linear Discrimination Analysis (LDA). The GA-AFCM approach was compared with traditional methods such as K-Means, Density Peaks, GMM, and MKKM-IC. The results demonstrate the TPR (91%), FPR (4%) precision (83.56%), accuracy (95.6%), and F1-score (87%) are used to examining and interpreting quickly and dynamically generated data streams efficiently solved by the proposed approach. The GA-AFCM method significantly enhances detection rates to over 95% while substantially reducing false positives, establishing it as a robust solution for cybersecurity in the big data era.