AI AND ZERO DAY ATTACK PREVENTION

Abstract

Zero-day attacks are a serious challenge for cyber security in modern era. This study presents a comprehensive AI-powered threat intelligence framework that integrates machine learning, natural language processing and anomaly detection to enable real-time analysis and predictive modelling of potential attack vectors. The framework incorporates automated response mechanisms to facilitate rapid mitigation of emerging threats. Experimental evaluation shows that the proposed system significantly improves both the speed and accuracy of zero-day attack detection compared to conventional approaches. The results show reduced detection latency and a higher true positive rate for detecting subtle attack behaviours. The findings suggest that AI-enhanced threat intelligence provides more adaptive and robust defences against evolving zero-day exploits, effectively transforming cyber security practices.