CoChat
Sign Up
Cited by 24Network Security and Intrusion DetectionSpam and Phishing DetectionSecurity and Verification in Computing

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

HoneyLLM: A Large Language Model-Powered Medium-Interaction Honeypot

Fan, Wenjun, Yang, Zichen, Liu, Yuanzhen, Qin, Lang, Liu, Jia·2025·Lecture Notes in Computer Science
DOI 10.1007/978-981-97-8801-9_13
Open this paper in CoChat — free account

Overview

Integrity signals

Registry

Crossref · 10.1007/978-981-97-8801-9_13

Cites

6 of 6 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 21

  1. Spitzner, L.: Honeypots: Tracking Hackers. Addison-Wesley Longman Publishing Co., Inc., Boston (2002)
  2. Fan, W., Zhihui, D., Fernández, D., Villagrá, V.A.: Enabling an anatomic view to investigate honeypot systems: a survey. IEEE Syst. J. 12(4), 3906–3919 (2018) · DOI
  3. Provos, N., Holz, T.: Virtual Honeypots: From Botnet Tracking to Intrusion Detection, 1st edn. Addison Wesley (2007)
  4. Jiang, X., Xu, D.: Collapsar: a VM-based architecture for network attack detention center. In: USENIX Security Symposium, pp. 15–28 (2004)
  5. Vrable, M., et al.: Scalability, fidelity, and containment in the Potemkin virtual honeyfarm. In: Proceedings of the Twentieth ACM Symposium on Operating Systems Principles - SOSP ’05, p. 148, Brighton, United Kingdom. ACM Press (2005) · DOI
  6. Provos, N.: A virtual honeypot framework. In: Proceedings of the 13th Conference on USENIX Security Symposium - Volume 13, SSYM’04, p. 1, Berkeley, CA, USA. USENIX Association (2004)
  7. Portokalidis, G., Slowinska, A., Bos, H.: Argos: an emulator for fingerprinting zero-day attacks for advertised honeypots with automatic signature generation. In: Proceedings of the 1st ACM SIGOPS/EuroSys European Conference on Computer Systems 2006, EuroSys ’06, pp. 15–27, New York, NY, USA. ACM (2006) · DOI
  8. Wu, S., Fan, W.: An SDN-NFV-enabled honeypot for manipulating command & control shell TCP connection. In: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium, pp. 1–5 (2023) · DOI
  9. Pearce, H., Tan, B., Ahmad, B., Karri, R., Dolan-Gavitt, B.: Examining zero-shot vulnerability repair with large language models. In: 2023 IEEE Symposium on Security and Privacy (SP), pp. 2339–2356 (2023) · DOI
  10. He, J., Vechev, M.: Large language models for code: security hardening and adversarial testing. In: Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security, pp. 1865–1879 (2023) · DOI
  11. Hu, P., Liang, R., Chen, H.: DeGPT: optimizing decompiler output with LLM. In: Proceedings 2024 Network and Distributed System Security Symposium (2024). https://api.semanticscholar.org/CorpusID, vol. 267622140 (2024) · DOI
  12. Fang, R., Bindu, R., Gupta, A., Zhan, Q., Kang, D.: LLM agents can autonomously hack websites. arXiv preprint arXiv:2402.06664 (2024) · arXiv
  13. Fang, R., Bindu, R., Gupta, A., Kang, D.: LLM agents can autonomously exploit one-day vulnerabilities. arXiv preprint arXiv:2404.08144 (2024) · arXiv
  14. Fang, R., Bindu, R., Gupta, A., Zhan, Q., Kang, D.: Teams of LLM agents can exploit zero-day vulnerabilities. arXiv preprint arXiv:2406.01637 (2024) · arXiv
  15. Deng, G., et al.: Pentestgpt: evaluating and harnessing large language models for automated penetration testing. In: USENIX Security Symposium (2024)
  16. TJ Dunham and Henry Syahputra. Reactor mk. 1 performances: MMLU, Humaneval and BBH test results. arXiv preprint arXiv:2406.10515, 2024 · arXiv
  17. Sun, W., Yuan, C., Fan, W.: A measurement of real-world attack connections toward honeypots. In: 2022 IEEE International Symposium on Measurements & Networking (M &N), pp. 1–6 (2022) · DOI
  18. Zhong, Z., Fan, W.: A honey-imprint enabled approach for resisting social engineering attacks. In: 2023 24th Asia-Pacific Network Operations and Management Symposium (APNOMS), pp. 302–305 (2023)
  19. McIntosh, T., et al.: Harnessing GPT-4 for generation of cybersecurity GRC policies: a focus on ransomware attack mitigation. Comput. Secur. 134, 103424 (2023) · DOI
  20. Akbar, K.A., et al.: Knowledge mining in cybersecurity: from attack to defense. In: Data and Applications Security and Privacy XXXVI, pp. 110–122 (2022) · DOI
  21. Wang, Z., et al.: HoneyGPT: breaking the trilemma in terminal honeypots with large language model. arXiv preprint arXiv:2406.01882 (2024) · arXiv

Cited by

Sorted by citations

Metadata

Authors
Fan, W., Yang, Z., Liu, Y., Qin, L., & Liu, J.
Affiliations
  • University of Liverpool (GB)
  • Xi’an Jiaotong-Liverpool University (CN)
Corresponding author
  • Fan, Wenjun
  • Yang, Zichen
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer Nature Singapore
Published
2024-12-25
Pages
253-272
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-981-97-8801-9_13
OpenAlex
W4405729564
Cited by
24
References
6
Citations by year
2026: 17 · 2025: 7
FWCI
10.51
Type
conference paper
Language
en
Open access
closed
Licence
Locations
Lecture notes in computer science (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
  • Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
  • Physical Sciences › Computer Science › Hardware and Architecture › Security and Verification in Computing
Keywords
honeypots, large language models, network attack detection, port scanning, cyber defense

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review