Authors: Muris Sladić, Veronica Valeros, Carlos Catania, Sebastian Garcia

Published on: August 31, 2023

Impact Score: 8.15

Arxiv code: Arxiv:2309.00155

Summary

• What is new: Use of Large Language Models (LLMs) to create dynamic and realistic software honeypots.
• Why this is important: Existing honeypots lack realism, making them easily recognizable by human attackers.
• What the research proposes: A novel method leveraging LLMs for creating dynamic, adaptable, and realistic honeypots.
• Results: The shelLM honeypot achieved a realism accuracy of 0.92 in tests with human attackers.

Technical Details

Technological frameworks used: Large Language Models

Models used: Not specified

Data used: Commands and interactions typical in honeypots

Potential Impact

Cybersecurity service providers, security software developers

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