| Presentation | 2022-11-24 Anomaly Detection on Web Pages Using HDBSCAN and Deep SVDD Yusuke Noji, Tomotaka Kimura, Jun Cheng, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In this paper, we propose an anomalous Web page detection method using Deep SVDD (Support Vector Data Description), which is one of deep learning methods. Although Deep SVDD assumes that most of the training data is normal data, the learning process is not stable because a certain percentage of abnormal web pages are included in the training data. Therefore, in this paper, we eliminate abnormal data by applying a clustering method before using Deep SVDD. Specifically, HDBSCAN (Hierarchical Density-based Spatial Clustering of Applications with Noise), a density-based clustering method, is used to remove anomalous data. Through experiments using a web page dataset, we show that HDBSCAN can remove anomalous data points and that the performance of Deep SVDD is stabilized by removing anomalous data. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | anomaly detection / machine learning / clustering / web pages |
| Paper # | CQ2022-51 |
| Date of Issue | 2022-11-17 (CQ) |
| Conference Information | |
| Committee | NS / ICM / CQ |
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| Conference Date | 2022/11/24(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Humanities and Social Sciences Center, Fukuoka Univ. + Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Network quality, Network measurement/management, Network virtualization, Network service, Blockchain, Security, Network intelligence/AI, etc. |
| Chair | Tetsuya Oishi(NTT) / Yuji Nomura(Fujitsu) / Jun Okamoto(NTT) |
| Vice Chair | Takumi Miyoshi(Shibaura Insti of Tech.) / Yu Miyoshi(NTT) / Eiji Takahashi(NEC) / Takefumi Hiraguri(Nippon Inst. of Tech.) / Gou Hasegawa(Tohoku Univ.) |
| Secretary | Takumi Miyoshi(NTT) / Yu Miyoshi(Kogakuin Univ.) / Eiji Takahashi(NTT) / Takefumi Hiraguri(Fujitsu) / Gou Hasegawa(NTT) |
| Assistant | Kotaro Mihara(NTT) / Ryo Yamamoto(Univ. of Electro-Comm) / Kimiko Kawashima(NTT) / Ryo Nakamura(Fukuoka Univ.) / Toshiro Nakahira(NTT) / Kenta Tsukatsune(Tokyo Metroplitan Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Network Systems / Technical Committee on Information and Communication Management / Technical Committee on Communication Quality |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Anomaly Detection on Web Pages Using HDBSCAN and Deep SVDD |
| Sub Title (in English) | |
| Keyword(1) | anomaly detection |
| Keyword(2) | machine learning |
| Keyword(3) | clustering |
| Keyword(4) | web pages |
| 1st Author's Name | Yusuke Noji |
| 1st Author's Affiliation | Doshisha University(Doshisha Univ.) |
| 2nd Author's Name | Tomotaka Kimura |
| 2nd Author's Affiliation | Doshisha University(Doshisha Univ.) |
| 3rd Author's Name | Jun Cheng |
| 3rd Author's Affiliation | Doshisha University(Doshisha Univ.) |
| Date | 2022-11-24 |
| Paper # | CQ2022-51 |
| Volume (vol) | vol.122 |
| Number (no) | CQ-275 |
| Page | pp.pp.23-27(CQ), |
| #Pages | 5 |
| Date of Issue | 2022-11-17 (CQ) |