A machine learning approach to dataset imputation for software vulnerabilities
Authors: Rostami, S., Kleszcz, A., Dimanov, D. and Katos, V.
Journal: Communications in Computer and Information Science
Volume: 1284 CCIS
Pages: 25-36
eISSN: 1865-0937
ISBN: 9783030589998
ISSN: 1865-0929
DOI: 10.1007/978-3-030-59000-0_3
Abstract: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.
https://eprints.bournemouth.ac.uk/34258/
Source: Scopus
A Machine Learning Approach to Dataset Imputation for Software Vulnerabilities
Authors: Rostami, S., Kleszcz, A., Dimanov, D. and Katos, V.
Conference: Multimedia Communications, Services & Security (MCSS'20)
Dates: 8 October-9 July 2020
Journal: Springer
https://eprints.bournemouth.ac.uk/34258/
Source: Manual
A Machine Learning Approach to Dataset Imputation for Software Vulnerabilities
Authors: Katos, V.
Conference: MCSS'20: 10th international Conference on Multimedia Communications, Services & Security
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.
https://eprints.bournemouth.ac.uk/34258/
Source: BURO EPrints