A3 Book section, Chapters in research books
Defensive Machine Learning Methods and the Cyber Defence Chain (2023)

Turtiainen, H., Costin, A., & Hämäläinen, T. (2023). Defensive Machine Learning Methods and the Cyber Defence Chain. In T. Sipola, T. Kokkonen, & M. Karjalainen (Eds.), Artificial Intelligence and Cybersecurity : Theory and Applications (pp. 147-163). Springer. https://doi.org/10.1007/978-3-031-15030-2_7

JYU authors or editors

Publication details

All authors or editors: Turtiainen, Hannu; Costin, Andrei; Hämäläinen, Timo

Parent publication: Artificial Intelligence and Cybersecurity : Theory and Applications

Parent publication editors: Sipola, Tuomo; Kokkonen, Tero; Karjalainen, Mika

ISBN: 978-3-031-15029-6

eISBN: 978-3-031-15030-2

Publication year: 2023

Publication date: 01/08/2022

Pages range: 147-163

Number of pages in the book: 301

Publisher: Springer

Place of Publication: Cham

Publication country: Switzerland

Publication language: English

DOI: https://doi.org/10.1007/978-3-031-15030-2_7

Publication open access: Not open

Publication channel open access: 

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/92691

Abstract

Cyberattacks are now occurring on a daily basis. As attacks and breaches are so frequent, and the fact that human work hours do not scale infinitely, the cybersecurity industry needs innovative and scalable tools and techniques to automate certain cybersecurity defensive tasks in order to keep up. The variety, the complex nature of the attacks, and the effectiveness of 0-day attacks mean that conventional tools are not adequate for securing complex networks with large numbers of users and endpoints with differing identities, behavior, and needs. Machine learning and artificial intelligence aid the creators of security tools in their tasks by introducing adaptive environment possibilities, customizability, and the ability to learn from past attacks and predict future attack attempts. In this chapter, we address innovations in machine learning, deep learning, and artificial intelligence within the defensive cybersecurity fields. We structure this chapter inline with the OWASP Cyber Defense Matrix in order to cover adequate grounds on this broad topic, and refer occasionally to the more granular MITRE D3FEND taxonomy whenever relevant.

Keywords: cyber security; data security; cyber attacks; machine learning; deep learning; artificial intelligence

Free keywords: machine learning methods; cyber defence chain

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2023

Preliminary JUFO rating: 2