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Cited by 13Adversarial Robustness in Machine LearningSpam and Phishing DetectionInformation and Cyber Security

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Act as a Honeytoken Generator! An Investigation into Honeytoken Generation with Large Language Models

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Reti, Daniel, Becker, Norman, Angeli, Tillmann, Chattopadhyay, Anasuya, Schneider, Daniel, Vollmer, Sebastian, Schotten, Hans D.·2024·Proceedings of the 11th ACM Workshop on Adaptive and Autonomous Cyber Defense
DOI 10.1145/3689935.3690394
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Abstract

With the increasing prevalence of security incidents, the adoption of deception-based defense strategies has become pivotal in cybersecurity. This work addresses the challenge of scalability in designing honeytokens, a key component of such defense mechanism. The manual creation of honeytokens is a tedious task. Although automated generators exists, they often lack versatility, being specialized for specific types of honeytokens, and heavily rely on suitable training datasets.

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Cited by

Sorted by citations

  • When LLMs meet cybersecurity: a systematic literature review

    Zhang, J., Bu, H., Wen, H., Liu, Y., Fei, H., Xi, R., Li, L., Yang, Y., Zhu, H., & Meng, D. (2025) · Cybersecurity · doi:10.1186/s42400-025-00361-w · Cited by 383

    “In their experiment, most people can’t recognize whether the remote host is a real one or a honeypot generated by LLMs. Reti et al. (2024) systematically investigates the use of LLMs to create a variety of honeytokens.”

Metadata

Authors
Reti, D., Becker, N., Angeli, T., Chattopadhyay, A., Schneider, D., Vollmer, S., & Schotten, H. D.
Affiliations
German Research Centre for Artificial Intelligence (DE)
ORCID
Venue
Proceedings of the 11th ACM Workshop on Adaptive and Autonomous Cyber Defense
Publisher
ACM
Published
2024-11-11
Pages
1-12
DOI
10.1145/3689935.3690394
OpenAlex
W4404991844
Cited by
13
References
3
Citations by year
2026: 4 · 2025: 8 · 2024: 1
FWCI
3.75
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
Federal Ministry of Education and Research (BMBF) (16KISK003K)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
  • Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
  • Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
Keywords
honeytokens, large language models, deception-based defense, cybersecurity, scalability, limited training data

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