A Machine Learning Approach to Dataset Imputation for Software Vulnerabilities

This paper proposes a supervised machine learning approach for the imputation of missing categorical values in a dataset where the majority of samples are incomplete. Twelve models have been designed that can predict nine of the twelve Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) tactic categories using only the Common Attack Pattern Enumeration and Classification (CAPEC). The proposed method has been evaluated on a test dataset consisting of 867 unseen samples, with the classification accuracy ranging from 99.88% to 100%. These models were employed to generate a more complete dataset with no missing ATT&CK tactic features.

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