LLM in the Shell: Generative Honeypots

J. Yuill, F. Wu, J. Settle, F. Gong, R. Forno, M. Huang, and J. Asbery, “Intrusion-detection for incident-response, using a military battlefield-intelligence process,” Computer Networks , vol. 34, no. 4, pp. 671–697, 2000

2000

Earlier work this paper cites.

L. Spitzner, “Honeypots: catching the insider threat,” in 19th Annual Computer Security Applications Conference, 2003. Proceedings. , 2003, pp. 170–179

2003

Earlier work this paper cites.

N. Provos et al. , “A virtual honeypot framework.” in USENIX Security Symposium , vol. 173, no. 2004, 2004, pp. 1–14

2004

Earlier work this paper cites.

T. Holz and F. Raynal, “Detecting honeypots and other suspicious environments,” in Proceedings from the Sixth Annual IEEE SMC Information Assurance Workshop , 2005, pp. 29–36

2005

Earlier work this paper cites.

T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” CoRR , vol. abs/2005.14165, 2020. [Online]. Available: https://arxiv.org/abs/2005.14165

Original

2005

Earlier work this paper cites.

G. Wicherski, “Medium interaction honeypots,” German Honeynet Project , 2006

2006

Earlier work this paper cites.

I. Mokube and M. Adams, “Honeypots: Concepts, approaches, and challenges,” in Proceedings of the 45th Annual Southeast Regional Conference , ser. ACM-SE 45. New York, NY, USA: Association for Computing Machinery, 2007, p. 321–326. [Online]. Available: https://doi.org/10.1145/1233341.1233399

2007

Earlier work this paper cites.

G. Wagener, R. State, T. Engel, and A. Dulaunoy, “Adaptive and self-configurable honeypots,” 05 2011, pp. 345–352

2011

Cited alongside, same era.

M. Oosterhof, “Cowrie,” 2014. [Online]. Available: https://github.com/cowrie/cowrie/

2014

Cited alongside, same era.

A. Pauna and I. Bica, “Rassh - reinforced adaptive ssh honeypot,” in 2014 10th International Conference on Communications (COMM) , 2014, pp. 1–6

2014

Cited alongside, same era.

S. Morishita, T. Hoizumi, W. Ueno, R. Tanabe, C. Gañán, M. J. van Eeten, K. Yoshioka, and T. Matsumoto, “Detect me if you… oh wait. an internet-wide view of self-revealing honeypots,” in 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM) , 2019, pp. 134–143

2019

Cited alongside, same era.

F. Setianto, E. Tsani, F. Sadiq, G. Domalis, D. Tsakalidis, and P. Kostakos, “Gpt-2c: a parser for honeypot logs using large pre-trained language models,” 11 2021, pp. 649–653

J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. Chi, Q. Le, and D. Zhou, “Chain-of-thought prompting elicits reasoning in large language models,” 2023

2023

Closest in time.

F. McKee and D. Noever, “Chatbots in a honeypot world,” 2023

2023

Closest in time.

OpenAI, “GPT 3.5,” https://platform.openai.com/docs/models/gpt-3-5 , 2023, [Online; accessed 22-July-2023]

2023

Closest in time.

N. F. Liu, K. Lin, J. Hewitt, A. Paranjape, M. Bevilacqua, F. Petroni, and P. Liang, “Lost in the middle: How language models use long contexts,” 2023

2023

Closest in time.

OpenAI, “Pricing,” https://web.archive.org/web/20230718183142/https://openai.com/pricing , 2023, [Online; accessed 9-December-2023]

2023

Closest in time.