Szczegóły publikacji
Opis bibliograficzny
A machine learning approach to dataset imputation for software vulnerabilities / Shahin Rostami, Agnieszka KLESZCZ, Daniel Dimanov, Vasilios Katos // W: Multimedia Communications, Services and Security : 10th international conference, MCSS 2020 : Kraków, Poland, October 8–9, 2020 : proceedings. — Cham : Springer Nature Switzerland, cop. 2020. — (Communications in Computer and Information Science ; ISSN 1865-0929 ; vol. 1284). — ISBN: 978-3-030-58999-8; e-ISBN: 978-3-030-59000-0. — S. 25-36. — Bibliogr. s. 35-36, Abstr. — Publikacja dostępna online od: 2020-09-24
Autorzy (4)
- Rostami Shahin
- AGHKleszcz-Rusek Agnieszka
- Dimanov Daniel
- Katos Vasilis
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 130772 |
|---|---|
| Data dodania do BaDAP | 2020-11-10 |
| DOI | 10.1007/978-3-030-59000-0_3 |
| Rok publikacji | 2020 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Czasopismo/seria | Communications in Computer and Information Science |
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. © Springer Nature Switzerland AG 2020.
