AI-Enhanced Threat Intelligence for Proactive Zero-Day Attack Detection

Introduction: zero-day attacks pose a critical cybersecurity challenge by targeting vulnerabilities that are undisclosed to software vendors and security experts. Conventional threat intelligence approaches, which rely on known signatures and attack patterns, often fail to detect these stealthy threats. Methods: this study proposes a comprehensive framework that combines AI technologies, including machine learning algorithms, natural language processing (NLP), and anomaly detection, to analyze threats in real time. The framework incorporates predictive modeling to anticipate potential attack vectors and automated response mechanisms to enable rapid mitigation. Results: the findings indicate that AI-enhanced threat intelligence significantly improves the detection of zero-day attacks compared to traditional methods. The framework reduces detection time and enhances accuracy by identifying subtle anomalies indicative of zero-day exploits. Conclusion: this research highlights the transformative potential of AI in strengthening threat intelligence against zero-day attacks. By leveraging advanced machine learning and real-time analytics, the proposed framework offers a more robust and adaptive approach to cybersecurity.