LLM in the Shell: Generative Honeypots

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00380657" target="_blank" >RIV/68407700:21230/24:00380657 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/EuroSPW61312.2024.00054" target="_blank" >https://doi.org/10.1109/EuroSPW61312.2024.00054</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/EuroSPW61312.2024.00054" target="_blank" >10.1109/EuroSPW61312.2024.00054</a>

Result language

angličtina

Original language name

LLM in the Shell: Generative Honeypots

Original language description

Honeypots are essential tools in cybersecurity for early detection, threat intelligence gathering, and analysis of attacker's behavior. However, most of them lack the required realism to engage and fool human attackers long-term. Being easy to distinguish honeypots strongly hinders their effectiveness. This can happen because they are too deterministic, lack adaptability, or lack deepness. This work introduces shelLM, a dynamic and realistic software honeypot based on Large Language Models that generates Linux-like shell output. We designed and implemented shelLM using cloud-based LLMs. We evaluated if shelLM can generate output as expected from a real Linux shell. The evaluation was done by asking cybersecurity researchers to use the honeypot and give feedback if each answer from the honeypot was the expected one from a Linux shell. Results indicate that shelLM can create credible and dynamic answers capable of addressing the limitations of current honeypots. ShelLM reached a TNR of 0.90, convincing humans it was consistent with a real Linux shell. The source code and prompts for replicating the experiments have been publicly available.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings - 9th IEEE European Symposium on Security and Privacy Workshops, Euro S and PW 2024

ISBN

979-8-3503-6729-4

ISSN

2768-0649

e-ISSN

2768-0657

Number of pages

6

Pages from-to

430-435

Publisher name

IEEE Computer Society

Place of publication

Cannes

Event location

Vídeň

Event date

Jul 8, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001302657400048