@article{oai:u-fukui.repo.nii.ac.jp:00024914,
 author = {清水, 大貴 and 小高, 知宏 and 黒岩, 丈介 and 諏訪, いずみ and 白井, 治彦 and Shimizu, Daiki and Odaka, Tomohiro and Kuroiwa, Jousuke and Suwa, Izumi and Shirai, Haruhiko},
 journal = {福井大学 大学院工学研究科 研究報告},
 month = {Mar},
 note = {In this paper, we proposed two effective feature extraction methods for detecting intrusion in HTTP request sequences. We compared the classification accuracy of each proposed method using a machine learning. Attacks on Web applications are difficult to distinguish between normal and abnormal, and mechanical detection is not easy. Therefore, we focused on the fact that attacks on various Web applications are closely related to special symbols that differ from ordinary characters. As a result of classification using a method characterized by the number of occurrences of special symbols, the accuracy rate was about 95%. Also F-value and AUC are about 94% each.},
 pages = {51--57},
 title = {機械学習を用いた Web アプリケーション攻撃検知手法の提案},
 volume = {68},
 year = {2020}
}