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A Machine Learning Approach to Dataset Imputation for Software Vulnerabilities.

Katos, 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: Unnamed user with email symplectic@symplectic
Deposited On:07 Jul 2020 09:16
Last Modified:10 Jul 2020 01:08

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