@inproceedings{db193081d46f448290bc95f87556028e,
title = "Auto-Labeling of Anomalies on Access Logs and Pairwise Comparison-based Validation",
abstract = "It is difficult to detect anomalous accesses only from the access log, lacking annotation and supervised learning-based models. In this paper, we propose auto-labeling methods for unlabeled access logs and present a validation method based on a pairwise comparison between them. We define two baseline methods for pairwise comparison: 1) office hour-based and 2) pattern-based methods. Then, we propose two methods based on unsupervised or semi-supervised learning: 1) iForest-based and 2) k -nearest neighbor(NN) based methods. Finally, we show that the k -NN-based method is effective in most cases.",
keywords = "Auto-labeling, Labeling validation, Unlabeled access logs",
author = "Jihoon Moon and Kwon, \{Hyuk Yoon\}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023 ; Conference date: 13-02-2023 Through 16-02-2023",
year = "2023",
doi = "10.1109/BigComp57234.2023.00079",
language = "English",
series = "Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "351--352",
editor = "Hyeran Byun and Ooi, \{Beng Chin\} and Katsumi Tanaka and Sang-Won Lee and Zhixu Li and Akiyo Nadamoto and Giltae Song and Young-guk Ha and Kazutoshi Sumiya and Wu Yuncheng and Hyuk-Yoon Kwon and Takehiro Yamamoto",
booktitle = "Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023",
}