学生のMAI, Trong Khangさん（博士後期課程3年、次世代デジタル社会基盤研究領域、BEURAN研究室）が、11th International Conference on Information Systems Security and Privacy (ICISSP 2025)においてBest Student Paper Awardを受賞しました。

　ICISSP 2025は、情報システムのセキュリティとプライバシーに関する国際会議で、令和7年2月20日～22日にかけてポルトガル（ポルト）で行われました。同会議では、研究者や技術者が一堂に会し、現代の情報システムのセキュリティ、プライバシー、信頼性に関する技術的、社会的、規制的課題など、最先端の研究成果について議論が行われました。
　Best Student Paper Awardは論文の質（プログラム委員会による評価）と口頭発表の質（会場でのセッション・チェアによるフィードバック）の両方を考慮し、同会議で発表された論文の著者（学生）に授与されるものです。

※参考：ICISSP 2026 (Previous Awards)

■受賞年月日
　令和7年2月22日

■研究題目、論文タイトル等
CyLLM-DAP: Cybersecurity Domain-Adaptive Pre-Training Framework of Large Language Models

■研究者、著者
Khang Mai, Razvan Florin Beuran, Naoya Inoue

■受賞対象となった研究の内容
Recently, powerful open-source models LLMs, such as Llama 3, have become alternatives to commercial ones, especially in sensitive or regulated industries. In cybersecurity, most LLM utilization relies on custom fine-tuning or post-training methods, such as prompt engineering. Although domain-adaptive pre-training has been proven to improve the model's performance in the specialized domain, it is less used in cybersecurity due to the cumbersome implementation effort. This paper introduces CyLLM-DAP, a framework for expediting the domain specialization process of LLMs in cybersecurity by simplifying data collecting, preprocessing, and pre-training stages in low-resource settings.
In this paper, we demonstrate how CyLLM-DAP can be utilized to collect, process data, and develop cybersecurity-specific LLMs (CyLLMs) based on state-of-the-art open-source models (Llama 3 and Mistral v0.3). The effectiveness of domain-adaptive pre-training is confirmed via two experiments for text classification and Q&A tasks. Our evaluation results show that, when compared with general base or instruct models, injecting the LLMs with cybersecurity knowledge allows the models to generally perform better in every fine-tuning epoch for the text classification task; and brings a performance gain of up to 4.75% for the Q&A task (comparable to domain-adaptive pre-training in other domains). The framework, the generated CyLLMs, and the data are publicly available for use in cybersecurity applications.

■受賞にあたって一言
Attending ICISSP 2025 in Porto and receiving the Best Student Paper Award are significant milestones of my study at JAIST. Although there were problems with connecting flights and presentation schedule, I successfully presented my paper and shared my work with cybersecurity researchers from around the world. Throughout the three days of the conference, I had the opportunity to communicate with other researchers and learn many things from them, which will help improve the quality of my future research.
I would like to thank my supervisors, Associate Professor Razvan Florin Beuran and Associate Professor Naoya Inoue, for their guidance regarding the research presented in this paper. I would also like to thank JAIST for providing the high-quality infrastructure (HPC system) that enabled this research.

　

　

令和7年3月13日