A recent study has shed light on the innovative use of honeypots, specially designed decoy systems that detect and analyze malicious activity, to enhance cybersecurity on cloud platforms. These honeypots, when strategically deployed, can provide valuable insights into attacker behavior, ultimately strengthening security measures.

The research proposes the creation of an interactive honeypot system using a Large Language Model (LLM) to mimic Linux server behavior. By refining and fine-tuning the LLM with a dataset of attacker-generated commands, the objective is to improve the effectiveness of honeypots in detecting and analyzing malicious activities.

To achieve this, the authors combined three datasets of Linux commands, including real-world attacker data, common commands, and command explanations. By simulating command execution and preprocessing the text, they created a robust dataset for training their language model to simulate a honeypot.

The study also involved prompt engineering to align prompts with research objectives and enhance the interaction of the model with the dataset, resulting in a more efficient honeypot system. The Llama3 8B model was selected for the honeypot LLM due to its balance of linguistic proficiency and computational efficiency.

The researchers fine-tuned a pre-trained language model using various techniques such as LoRA, QLoRA, NEFTune noise, and Flash Attention 2 to enhance training efficiency and performance. This led to the development of a honeypot server-like model that could interact with attackers in natural language, enabling realistic simulation and analysis of attacker behavior.

The custom SSH server, built using Python’s Paramiko library, utilizes the fine-tuned language model to generate realistic responses to user commands. It logs SSH connections, user credentials, and command interactions, providing valuable data for cybersecurity analysis.

The training losses of the fine-tuned model exhibited a consistent decline, indicating effective learning from the dataset. By using a learning rate of 5×10−4 for 36 training steps, the model demonstrated consistent performance improvement and the ability to generate realistic and contextually appropriate responses.

In terms of performance, the fine-tuned model outperformed the base model, as evidenced by consistently higher similarity scores and lower distance metrics across all samples. This indicates the model’s effectiveness in generating outputs that closely align with expected responses from a Cowrie honeypot server.

The paper introduces a new method for creating interactive and realistic honeypot systems using LLMs, which can significantly improve threat detection and provide deeper insights into attacker behavior. The researchers plan to expand training datasets, explore alternative fine-tuning techniques, and incorporate behavioral analysis to further enhance the honeypot system.

By deploying the system publicly to collect attack logs and create knowledge graphs for analyzing attacker strategies, the researchers aim to refine the model based on performance metrics like accuracy and interaction quality. Ultimately, these advancements aim to bolster honeypots for better cyber-threat detection and analysis in the realm of cybersecurity.

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