Machine learning methods for classification and prediction information security risk assessment

Abstract

Information is an essential company asset that must be protected. The value of information assets depends on the type and scale of the business and its role in delivering services. One of the primary programs that can help identify areas of improvement and guide the development of security awareness programs is risk assessment. Managing cybersecurity risks is critical to protecting enterprises from developing cyber threats and promoting resilience. This includes detecting, assessing, and mitigating risks to protect sensitive data, systems, and networks. While cybersecurity risk management is challenging, organizations may improve their security posture. This paper seeks to contribute to the field of information security risk assessment by leveraging the power of machine learning to provide quick, cost-effective, and individualized risk assessments for small and medium enterprises. Specifically, we extend the evaluation for security level classification by utilizing a support vector machine, random forest, and gradient boosting algorithms. The results demonstrate how well the model detects significant cases while reducing false positives. The model’s exceptional precision ensures that its identifications are dependable, while the high recall demonstrates that it accurately detects relevant data. Precision is critical in security risk assessment because a false positive result might have profound effects.