Genetic Algorithm-based Fuzzy Soft Computing in Cybersecurity: A Review
Abstract
With the rapid digitization of personal, financial, healthcare, and governmental infrastructures, cybersecurity has emerged as a critical global priority. As cyber threats become increasingly sophisticated, dynamic, and unpredictable, traditional intrusion detection systems (IDS) face significant limitations. Most conventional IDS models rely on fixed rule-based mechanisms and binary machine learning classifiers that categorize activities strictly as either legitimate or malicious. This rigid decision-making approach often fails to effectively address the uncertainty, ambiguity, and incomplete information commonly present in real-world network traffic. As a result, such systems tend to generate high false positive rates and struggle to detect novel or evolving attack patterns. Although several anomaly detection techniques have been introduced, many lack adaptability and the capability to manage imprecise data efficiently. To overcome these challenges, this study proposes a genetic algorithm-based fuzzy soft computing approach for cybersecurity. By integrating fuzzy logic’s strength in handling uncertainty with the optimization capability of genetic algorithms, the proposed framework aims to improve detection accuracy, minimize false alarms, and enhance adaptability. This research contributes toward developing a more intelligent, flexible, and robust intrusion detection system suitable for securing modern digital environments against continuously evolving cyber threats.
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