Abstract

The honeypot is a tool to detect the attacker's activity and can be used as a diversion. But the growth of attacking techniques makes the attacker realize they are interacting with a honeypot, not a real server. Previous research discusses the concept of an adaptive honeypot to make the honeypot more adaptive to the attacker because the growth of cyber attack techniques make attacker realize the presence of the honeypot. To realize the idea, researchers combine the honeypot with the reinforcement learning (RL) algorithm to create a honeypot that can learn from attacker behavior. In this research, we measure the impact of the RL algorithm on the SSH honeypot. The result is that the attacker and honeypot interaction is longer than before.

Original languageEnglish
Title of host publicationProceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering
Subtitle of host publicationApplying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages346-350
Number of pages5
ISBN (Electronic)9798350399615
DOIs
Publication statusPublished - 2022
Event6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022 - Virtual, Online, Indonesia
Duration: 13 Dec 202214 Dec 2022

Publication series

NameProceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022

Conference

Conference6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022
Country/TerritoryIndonesia
CityVirtual, Online
Period13/12/2214/12/22

Keywords

  • Cowrie
  • adaptive honeypot
  • honeypot
  • reinforcement learning

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