Leveraging Artificial Intelligence for Predictive CyberSecurity: Enhancing Threat Forecasting and Vulnerability Management

Abstract

The rise in sophisticated cyber threats demands advanced cybersecurity methods that surpass traditional rule-based approaches. This study explores the application of Artificial Intelligence (AI) to enhance predictive cybersecurity, enabling more accurate threat forecasting and effective vulnerability management. The research assesses various AI
modelssuch as neural networks, decision trees, and Support Vector Machines (SVMs) in their ability to predict cyber threats. Employing a quantitative methodology, the study utilizes historical data from cybersecurity sources, threat intelligence feeds, vulnerability logs, and incident reports. Key performance metrics, including accuracy, precision, recall, F1-score, and Receiver Operating Characteristic - Area Under the Curve (ROC-AUC), were used to test, validate, and train the AI models. Neural networks emerged as the most accurate, achieving 93% accuracy, particularly excelling in identifying phishing attacks and zero-day vulnerabilities. SVM models also performed well, minimizing false positives and increasing detection rates, while decision trees proved computationally efficient and easily interpretable in simpler cybersecurity scenarios. The findings underscore the superiority of AI models over traditional methods, offering dynamic solutions for evolving cyber threats. This research contributes to the field by demonstrating the extensive potential of AI in predictive cybersecurity, providing actionable insights for organizations implementing AI-driven threat detection and vulnerability management.