Asguard: Adaptive Self-guarded Honeypot

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

Abstract

Cybersecurity is of critical importance to any organisations on the Internet, with attackers exploiting any security loopholes to attack them. To combat cyber threats, a honeypot, a decoy system, has been an effective tool used since 1991 to deceive and lure attackers to reveal their attacks. However, these tools become increasingly easy to detect, which diminishes their usefulness. Recently, adaptive honeypots, which can change their behaviour in response to attackers, have emerged: despite their promise, however, they still have some shortcomings of their own. In this paper we survey conventional and adaptive honeypots and discuss their limitations. We introduce an approach for adaptive honeypots that uses Q-learning, a reinforcement learning algorithm, to effectively achieve two objectives at the same time: (1) learn to engage with attacker to collect their attack tools and (2) guard against being compromised by combining state environment and action to form a new reward function.
Original languageEnglish
Title of host publicationProceedings of the 17th International Conference on Web Information Systems and Technologies
PublisherSciTePress
Pages565-574
Number of pages10
Volume1
ISBN (Electronic)978-989-758-536-4, 2184-3252
DOIs
Publication statusPublished - Oct 2021
Event17th International Conference on Web Information Systems and Technologies - Volume 1: DMMLACS, - Online streaming
Duration: 26 Oct 202128 Oct 2021
Conference number: 17
https://webist.scitevents.org/?y=2021

Publication series

NameProceedings of the 17th International Conference on Web Information Systems and Technologies

Conference

Conference17th International Conference on Web Information Systems and Technologies - Volume 1: DMMLACS,
Abbreviated titleWEBIST 2021
Period26/10/2128/10/21
Internet address

Keywords

  • Adaptive Honeypot
  • Multiple-objective Honeypot
  • Reinforcement learning
  • Intelligent Honeypot

Library Keywords

  • Cybersecurity
  • Security

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