Fairness in Machine Learning for Cybersecurity: Enhancing Trust Through Feature Importance and SHAP Analysis

This paper addresses the issue of fairness in machine learning (ML) models within the cybersecurity domain, focusing on the interpretability and trustworthiness of model decisions. By applying feature importance analysis and SHAP (SHapley Additive exPlanations) values to a ransom ware detection dataset, we identify and mitigate unintended biases that can compromise the security of these models. The findings reveal that certain features, such as file names and DLL characteristics, disproportionately influence model predictions, creating vulnerabilities that attackers can exploit. Furthermore, the potential of Generative Adversarial Networks (GANs) to strengthen model robustness against adversarial attacks is explored. The approach presented offers notable improvements in the transparency and reliability of ML models within the cybersecurity domain. These insights provide practical guidelines for researchers to develop fairer and more resilient ML models for cybersecurity applications.