Developing a New Feature for Vulnerability Risk Scoring Model for Enhanced Cybersecurity

Abstract

As organisations increasingly rely on technology, the risk of cyber threats to data integrity and security grows significantly. Traditional vulnerability risk-scoring models may not adequately address the rapidly evolving nature of cyber threats, necessitating the development of more adaptable and context-specific models. This research aims to achieve two primary objectives: developing a flexible risk-scoring model that can be customised for different industries, companies, or situations, and creating a new feature that accurately reflects the risk score based on the current dataset. The study employs correlation analysis and machine learning-based regression modelling, utilising appropriate evaluation metrics to assess model performance. Results indicate that the K-Nearest Neighbors regression model performs particularly well, offering precise risk assessments. The risk score produced by the model serves as a critical tool for prioritising cybersecurity efforts, where higher scores denote a greater need for immediate action. This research contributes to the field by providing a scalable and customisable framework for developing specialised risk-scoring models, enhancing the effectiveness of cybersecurity strategies across diverse contexts.