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    A Cybersecurity Procedure to Vulnerabilities Classification of Windows OS Based on Feature Selection and Machine Learning
    (Springer International Publishing Ag, 2024) Al-Sarray, Noor Alhuda Abdul Hasan; Demir, Sait
    The fast advancement of systems and technology has led to problems in several areas, with data protection and information security being particularly significant concerns. Cybersecurity is a recently developed field that comprises many strategies aimed at safeguarding sensitive data. By utilizing machine learning techniques, this study has improved the security of the Windows operating system. The categorization of Windows system vulnerabilities was performed using machine learning techniques such as Random Forest, Logistic Regression, Naive Bayes, K-Nearest Neighbors, and Support Vector Machine. The dataset was compiled using materials from the National Institute of Standards and Technology (NIST) and exploit-deb, Care was taken to ensure that the data is real and exists. The parameters that were assessed include accuracy, precision, recall, f1-score, and ROC AUC Score. The Random Forest approach yielded the most precise findings, with an accuracy rate of 97%. The findings indicated that the Random Forest methodology was successful in identifying vulnerabilities in security.