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.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2025.4
- 수록면
- 647 - 665 (19page)
- DOI
- 10.7840/kics.2025.50.4.647
이용수
초록· 키워드
오류제보하기
In this paper, we implemented an anomaly detection system based on user behavior analysis to effectively detect user anomalies in a specific domain. For this purpose, we performed EDA on User Behavior Data, defined feature factors for user behavior features in feature engineering, and proposed a method for combining feature factors. We performed preprocessing and vectorization on the session data to define user behavior patterns as 'Session' and provide them as input to the model. The vectorized session data was pre-trained on BERT Model Architecture using only normal session data. We performed an anomaly detection performance evaluation after fine-tuning using normal and abnormal session data. As a result of the performance evaluation, BERT-Medium-uncased model performed well with an Accuracy of 0.9630 and an F1-Score of 0.9628, and the overall performance was balanced. As a result, we confirmed that by utilizing EDA and feature engineering for data, we can effectively perform pre-training and fine-tuning and implement a high-performance anomaly detection system.
목차
ABSTRACT
Ⅰ. Introduction
Ⅱ. Related Works
Ⅲ. Method
Ⅳ. Experimental Results
Ⅴ. Conclusion
References
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