| Title | Reinforcement Learning for Anti-Ransomware Testing |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Adamov, Alexander, Carlsson, Anders |
| Conference Name | 2020 IEEE East-West Design Test Symposium (EWDTS) |
| Keywords | anti-ransomware testing, artificial intelligence, composability, cryptography, Detectors, encoding, Games, machine learning, Metrics, pubcrawl, ransomware, reinforcement learning, Resiliency, Testing |
| Abstract | 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. |
| DOI | 10.1109/EWDTS50664.2020.9225141 |
| Citation Key | adamov_reinforcement_2020 |
Reinforcement Learning for Anti-Ransomware Testing