AUTOMATED VULNERABILITY PRIORITIZATION AND REMEDIATION USING DEEP LEARNING

Abstract

Effective vulnerability management is essential in the quickly developing field of cybersecurity to protect information systems from new attacks. Conventional methods, such rule-based expert systems and the Common Vulnerability Scoring System (CVSS), frequently find it difficult to keep up with the changing landscape of vulnerabilities. This work presents a novel use of convolutional neural networks (CNNs) supplemented with attention mechanisms for automated vulnerability prioritisation and remediation. The suggested deep learning approach increases the precision and effectiveness of vulnerability management by utilising contextual analysis and sophisticated feature extraction.

The study includes a thorough analysis of how well the CNN model performs in comparison to conventional techniques. The CNN model ranked vulnerabilities with a 92% accuracy rate and suggested remediation steps with an 87% accuracy rate. The model's outstanding ability to distinguish between high-risk and low-risk vulnerabilities is shown by its 0.95 AUC-ROC score. Traditional rule-based systems, on the other hand, showed worse performance metrics, with 75% and 68% accuracy rates for prioritisation and remediation, respectively. Furthermore, the CNN model improved its practical usefulness in real-time applications by drastically reducing processing times. The outcomes highlight how deep learning can be used to overcome the drawbacks of static and rule-based methods. The suggested architecture offers a strong response to current cybersecurity issues by giving vulnerability management a more accurate and adaptable framework.