Self-propagating Malware Containment via Reinforcement Learning

Self-propagating Malware Containment via Reinforcement Learning

Abstract

We introduce a reinforcement learning based containment system for self-propagating malware in local networks. The system is trained with real-world software and malware and leverages a network of virtual machines for execution and propagation. Instead of relying on labels as is common with supervised learning, we follow a trial-and-error approach in order to learn how to link network traffic to malware infections.

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Authors
  • Eresheim, Sebastian
  • Pasterk, Daniel
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Shortfacts
Category
Paper in Conference Proceedings or in Workshop Proceedings (Paper)
Event Title
Machine Learning and Knowledge Extraction
Divisions
Security and Privacy
Subjects
Computersicherheit
Angewandte Informatik
Event Location
Virtual Event
Event Type
Conference
Event Dates
17-20 Aug 2021
Publisher
Springer International Publishing
Page Range
pp. 35-50
Date
2021
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