A Study on Data Reconstruction and Model Parameter Optimization for Implementation of Anomaly Detection System Based on User Behavior Analysis 


Vol. 50,  No. 4, pp. 647-665, Apr.  2025
10.7840/kics.2025.50.4.647


PDF Full-Text
  Abstract

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.

  Statistics
Cumulative Counts from November, 2022
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.


  Related Articles
  Cite this article

[IEEE Style]

Y. So, J. Park, K. Kang, "A Study on Data Reconstruction and Model Parameter Optimization for Implementation of Anomaly Detection System Based on User Behavior Analysis," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 4, pp. 647-665, 2025. DOI: 10.7840/kics.2025.50.4.647.

[ACM Style]

Yu-Jin So, Jong-Geun Park, and Kyuchang Kang. 2025. A Study on Data Reconstruction and Model Parameter Optimization for Implementation of Anomaly Detection System Based on User Behavior Analysis. The Journal of Korean Institute of Communications and Information Sciences, 50, 4, (2025), 647-665. DOI: 10.7840/kics.2025.50.4.647.

[KICS Style]

Yu-Jin So, Jong-Geun Park, Kyuchang Kang, "A Study on Data Reconstruction and Model Parameter Optimization for Implementation of Anomaly Detection System Based on User Behavior Analysis," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 4, pp. 647-665, 4. 2025. (https://doi.org/10.7840/kics.2025.50.4.647)
Vol. 50, No. 4 Index

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)






pISSN : 1226 - 4717
eISSN : 2287 - 3880