인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
In this paper, we propose a Large Language Model (LLM) pipeline utilizing the UWF-ZeekData22 dataset based on MITRE ATT&CK Matrix to address the growing cyber threats in modern society. We first performed an exploratory data analysis (EDA) to derive key feature groups that reflect the spatio-temporal characteristics and connectivity of network traffic logs. The derived feature groups are used to generate input sequences for pre-training the BERT model. In the pre-training phase, we applied a masked language model (MLM) task to effectively learn network traffic patterns and achieved a mask prediction accuracy of over 0.9. In the fine-tuning and inference phase, we optimized the models for anomaly detection by adopting a weighted sampling technique to handle the imbalance problem of each tactic in the dataset. The performance evaluation showed that all models had an accuracy above 0.94 and an AUC-ROC value close to 1.0. We also analyzed the impact of the padding method according to model size and found that static padding performed better for large models, while dynamic padding performed better for small models. These results demonstrate that LLM-based pre-training can successfully learn complex patterns of network traffic logs and can reliably detect various tactics. Therefore, the proposal of this paper is expected to provide a practical case study in the modernization of network security systems and the development of real-time security monitoring solutions.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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UCI(KEPA) : I410-151-26-02-094278023
