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Lessons learned from the deployment of a high-interaction honeypot
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The Journal of Machine Learning Research
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ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
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ICDCIT'04 Proceedings of the First international conference on Distributed Computing and Internet Technology
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RAID'06 Proceedings of the 9th international conference on Recent Advances in Intrusion Detection
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In this article we describe a new paradigm for adaptive honeypots that are capable of learning from their interaction with attackers. The main objective of such honeypots is to get as much information as possible about the profile of an intruder, while decoying their true nature and goals. We have leveraged machine learning techniques for this task and have developed a honeypot that uses a variant of reinforcement learning in order to learn the best behavior when facing attackers. The honeypot is capable of adopting behavioral strategies that vary from blocking commands, returning erroneous messages right up to insults that aim to irritate the intruder and serve as reverse Turing Test. Our preliminary experimental results show that behavioral strategies are dependent on contextual parameters and can serve as advanced building blocks for intelligent honeypots.