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Using Reinforcement Learning to Conceal Honeypot Functionality

  • Seamus DowlingEmail author
  • Michael Schukat
  • Enda Barrett
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11053)

Abstract

Automated malware employ honeypot detecting mechanisms within its code. Once honeypot functionality has been exposed, malware such as botnets will cease the attempted compromise. Subsequent malware variants employ similar techniques to evade detection by known honeypots. This reduces the potential size of a captured dataset and subsequent analysis. This paper presents findings on the deployment of a honeypot using reinforcement learning, to conceal functionality. The adaptive honeypot learns the best responses to overcome initial detection attempts by implementing a reward function with the goal of maximising attacker command transitions. The paper demonstrates that the honeypot quickly identifies the best response to overcome initial detection and subsequently increases attack command transitions. It also examines the structure of a captured botnet and charts the learning evolution of the honeypot for repetitive automated malware. Finally it suggests changes to an existing taxonomy governing honeypot development, based on the learning evolution of the adaptive honeypot. Code related to this paper is available at: https://github.com/sosdow/RLHPot.

Keywords

Reinforcement learning Honeypot Adaptive 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Galway Mayo Institute of TechnologyCastlebar, MayoIreland
  2. 2.National University of Ireland GalwayGalwayIreland

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