A Hybrid Ai and Mathematical Modelling Approach for Adaptive Network Security And Intrusion Prevention

Abstract

The nature of attacks on modern communication networks is becoming more complex, making static and rule based intrusions systems less effective in providing protection. Approach of Artificial Intelligence and Mathematical modelling proposed in this study for Adaptive Network security and Intrusion Prevention. It integrates artificial intelligence (AI) based traffic classification with mathematical threat detection algorithms to detect malicious traffic and establish appropriate security measures. The primary inputs are five network parameters: packet rate, average packet size, connection frequency, failed connection ratio and traffic anomaly index. The AI part determines the probability of intrusion and category of attack, while the mathematical model calculates a hybrid threat score of attack probability, attack severity, attack frequency, anomaly level and network vulnerability. The system determines adaptive actions to trigger, based on the computed score (blocking, monitoring, isolation, rate limiting). The accuracy, precision, recall, F1-score, false-positive rate, prevention success rate and response latency are used for performance evaluation and it allows for interpretable and adaptive decisions about intrusion prevention in various networks.