AI-Driven Approaches for the Early Identification of Zero-Day Vulnerabilities in Cybersecurity Systems

Abstract

Zero-day vulnerabilities represent one of the most critical threats in cybersecurity, often going undetected until exploited by malicious actors. This paper explores AI-driven approaches for the early identification and mitigation of zero-day vulnerabilities in cybersecurity systems. By employing advanced machine learning algorithms, including anomaly detection, neural networks, and natural language processing, the study focuses on identifying patterns and behaviors indicative of previously unknown security weaknesses. The research leverages both supervised and unsupervised learning models to analyze vast amounts of network traffic, system logs, and codebase behaviors, aiming to uncover subtle, previously unseen vulnerabilities before they are exploited. The paper evaluates the effectiveness of AI models in reducing detection time, enhancing predictive capabilities, and minimizing false positives. Results demonstrate that AI-driven techniques, when integrated with traditional vulnerability management frameworks, significantly improve the proactive identification of zero-day vulnerabilities, enabling more timely responses and reducing the risk of exploitation. This work underscores the potential of AI in reshaping cybersecurity practices, providing a more resilient and adaptive defense against the evolving landscape of cyber threats.