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Reinforcement Learning for Anti-Ransomware Testing
Kharkiv National University of Radio Electronics, UKR.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0002-9328-9143
2020 (English)In: 2020 IEEE East-West Design and Test Symposium, EWDTS 2020 - Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2020, article id 9225141Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we are going to verify the possibility to create a ransomware simulation that will use an arbitrary combination of known tactics and techniques to bypass an anti-malware defense. To verify this hypothesis, we conducted an experiment in which an agent was trained with the help of reinforcement learning to run the ransomware simulator in a way that can bypass anti-ransomware solution and encrypt the target files. The novelty of the proposed method lies in applying reinforcement learning to anti-ransomware testing that may help to identify weaknesses in the anti-ransomware defense and fix them before a real attack happens. © 2020 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2020. article id 9225141
Keywords [en]
anti-ransomware testing, artificial intelligence, machine learning, ransomware, reinforcement learning, Network security, Anti-malware, Malware
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-20811DOI: 10.1109/EWDTS50664.2020.9225141Scopus ID: 2-s2.0-85096415455ISBN: 9781728198996 (print)OAI: oai:DiVA.org:bth-20811DiVA, id: diva2:1506573
Conference
2020 IEEE East-West Design and Test Symposium, EWDTS 2020, Varna, Bulgaria, 4 September 2020 through 7 September 2020
Note

open access

Available from: 2020-12-03 Created: 2020-12-03 Last updated: 2021-10-06Bibliographically approved

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fulltext(631 kB)587 downloads
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Carlsson, Anders

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