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ToolsKatos, V., 2020. A Machine Learning Approach to Dataset Imputation for Software Vulnerabilities. In: MCSS'20: 10th international Conference on Multimedia Communications, Services & Security, 8-9 July 2020, Krakow, Poland.
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Abstract
This paper proposes a supervised machine learning approach for the imputation of missing categorical values from the majority of samples in a dataset. Twelve models have been designed that are able to predict nine of the twelve ATT&CK tactic categories using only one feature, namely the Common Attack Pattern Enumeration and Classification (CAPEC). The proposed method has been evaluated on a 867 sample unseen test set with classification accuracy in the range of 99.88%- 100%. Using these models, a more complete dataset has been generated with no missing values for the ATT&CK tactic feature.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Group: | Faculty of Science & Technology |
| ID Code: | 34258 |
| Deposited By: | Symplectic RT2 |
| Deposited On: | 07 Jul 2020 09:16 |
| Last Modified: | 14 Mar 2022 14:23 |
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