LLM Honeypot: Leveraging Large Language Models as Advanced Interactive Honeypot Systems
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909375387402240 |
|---|---|
| author | Otal, Hakan T. Canbaz, M. Abdullah |
| author_facet | Otal, Hakan T. Canbaz, M. Abdullah |
| contents | The rapid evolution of cyber threats necessitates innovative solutions for detecting and analyzing malicious activity. Honeypots, which are decoy systems designed to lure and interact with attackers, have emerged as a critical component in cybersecurity. In this paper, we present a novel approach to creating realistic and interactive honeypot systems using Large Language Models (LLMs). By fine-tuning a pre-trained open-source language model on a diverse dataset of attacker-generated commands and responses, we developed a honeypot capable of sophisticated engagement with attackers. Our methodology involved several key steps: data collection and processing, prompt engineering, model selection, and supervised fine-tuning to optimize the model's performance. Evaluation through similarity metrics and live deployment demonstrated that our approach effectively generates accurate and informative responses. The results highlight the potential of LLMs to revolutionize honeypot technology, providing cybersecurity professionals with a powerful tool to detect and analyze malicious activity, thereby enhancing overall security infrastructure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_08234 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LLM Honeypot: Leveraging Large Language Models as Advanced Interactive Honeypot Systems Otal, Hakan T. Canbaz, M. Abdullah Cryptography and Security Artificial Intelligence Computation and Language Machine Learning Networking and Internet Architecture 68T50, 68M10 I.2.7; D.4.6; K.6.5 The rapid evolution of cyber threats necessitates innovative solutions for detecting and analyzing malicious activity. Honeypots, which are decoy systems designed to lure and interact with attackers, have emerged as a critical component in cybersecurity. In this paper, we present a novel approach to creating realistic and interactive honeypot systems using Large Language Models (LLMs). By fine-tuning a pre-trained open-source language model on a diverse dataset of attacker-generated commands and responses, we developed a honeypot capable of sophisticated engagement with attackers. Our methodology involved several key steps: data collection and processing, prompt engineering, model selection, and supervised fine-tuning to optimize the model's performance. Evaluation through similarity metrics and live deployment demonstrated that our approach effectively generates accurate and informative responses. The results highlight the potential of LLMs to revolutionize honeypot technology, providing cybersecurity professionals with a powerful tool to detect and analyze malicious activity, thereby enhancing overall security infrastructure. |
| title | LLM Honeypot: Leveraging Large Language Models as Advanced Interactive Honeypot Systems |
| topic | Cryptography and Security Artificial Intelligence Computation and Language Machine Learning Networking and Internet Architecture 68T50, 68M10 I.2.7; D.4.6; K.6.5 |
| url | https://arxiv.org/abs/2409.08234 |