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Paper Abstract and Keywords
Presentation 2022-11-24 13:25
Anomaly Detection on Web Pages Using HDBSCAN and Deep SVDD
Yusuke Noji, Tomotaka Kimura, Jun Cheng (Doshisha Univ.) CQ2022-51
Abstract (in Japanese) (See Japanese page) 
(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) 
(in English) anomaly detection / machine learning / clustering / web pages / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 275, CQ2022-51, pp. 23-27, Nov. 2022.
Paper # CQ2022-51 
Date of Issue 2022-11-17 (CQ) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF CQ2022-51

Conference Information
Committee NS ICM CQ NV  
Conference Date 2022-11-24 - 2022-11-25 
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. 
Paper Information
Registration To CQ 
Conference Code 2022-11-NS-ICM-CQ-NV 
Language Japanese 
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  
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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.)
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Speaker Author-1 
Date Time 2022-11-24 13:25:00 
Presentation Time 25 minutes 
Registration for CQ 
Paper # CQ2022-51 
Volume (vol) vol.122 
Number (no) no.275 
Page pp.23-27 
#Pages 5 
Date of Issue 2022-11-17 (CQ) 

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