A Novel Soft Computing-Driven Entropy-Fuzzy Rule Optimization Framework for Early Adaptive Intrusion Detection

This paper proposes the Entropy-Fuzzy Rule Optimized Synergistic Tuning Framework for Intrusion Detection System (E-FROST IDS), an entropy-driven, fuzzy-rule-optimization-enhanced intrusion detection system designed to address challenges related to model accuracy, flexibility, and uncertainty. The results suggest that E-FROST improves the effectiveness of anomaly detection, reduces false alarms, and enhances interpretability through entropy-based uncertainty modeling. The novelty lies in integrating fuzzy entropy with an efficient decision engine tailored for dynamic threat environments. The main contribution is a unified and adaptive IDS capable of identifying threats across a wide range of network conditions. Major implications include strengthened cyber-defense preparedness, greater transparency for security analysts, and a flexible framework for future intelligent intrusion detection system solutions.