The authors propose a novel approach to honeypot creation using Large Language Models. By fine-tuning a pre-trained LLM on a diverse dataset of attacker interactions, they developed a honeypot capable of realistic and sophisticated engagement with attackers.
Data collection, prompt engineering, model selection, and supervised fine-tuning were all techniques that were utilized in the methodology.
Evaluation results demonstrate the effectiveness of the approach in generating accurate and informative responses, highlighting the potential of LLMs to revolutionize honeypot technology and enhance cybersecurity.
The LLM-Honeypot system has the potential to significantly enhance the effectiveness of traditional honeypots by engaging with attackers in a more sophisticated and informative manner, which could lead to improved detection and analysis of malicious activities, ultimately strengthening cybersecurity defenses.
It is a novel approach to analyzing malicious activities using a Supervised Fine-Tuned (SFT) Language Model (LLM) as a honeypot, where the LLM was trained on a dataset comprising real-world attacker commands, common Linux commands, and command explanations to enhance its ability to mimic a Linux server.
After undergoing extensive testing, the honeypot that had been optimized was put into operation so that it could interact with potential dangers in a public setting.
The dataset was processed, including tokenization and standardization, to ensure effective training of the LLM, ultimately resulting in a realistic and interactive honeypot capable of engaging with attackers and providing valuable security insights.
The study carefully engineered prompts to optimize the LLM’s interaction with the dataset, ensuring effective honeypot simulation. After evaluating various models, Llama3 8B was selected for its balance of linguistic proficiency and computational efficiency.
Supervised Fine-Tuning (SFT) with Low-Rank Adaptation (LoRA) was employed to adapt the model to the specific task, enhancing training efficiency while preserving performance, which resulted in a highly effective honeypot capable of realistic and informative interactions with attackers.
The experiment details the technical aspects of the honeypot system and evaluation methods, as well as building a custom SSH server using Python’s Paramiko library to integrate their LLM for realistic responses.
They evaluated the fine-tuned model’s performance using various metrics like cosine similarity, Jaro-Winkler similarity, and Levenshtein distance. The results showed that the fine-tuned LLM achieved better similarity scores compared to the baseline model, indicating its effectiveness in mimicking a Linux server.
The training loss also steadily decreased during the training process, demonstrating the model’s capability to learn and improve.
The paper proposes a novel approach to honeypot development using Large Language Models (LLMs). By fine-tuning a pre-trained LLM on attacker data, the researchers created a more realistic and effective honeypot system.
LLM’s reinforcement learning and attention mechanisms enhance response quality and contextual relevance, providing deeper insights into attacker behavior.
Future work includes expanding training datasets, exploring alternative fine-tuning methods, and incorporating behavioral analysis to further refine the honeypot system for improved cyber-threat detection and analysis.
