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Paper Abstract and Keywords
Presentation 2019-11-08 13:30
Malicious Domain Names Detection Based on TF-IDE and Variational Autoencoder: Classification with Quantum-enhanced Support Vector Machine
Yuwei Sun (UTokyo), Ng S. T. Chong (UNU), Hideya Ochiai (UTokyo) KBSE2019-26 SC2019-23
Abstract (in Japanese) (See Japanese page) 
(in English) With the development of network technology, the use of domain name system (DNS) becomes common. However, the attacks on the internet like using a zombie network, are also surging. The domain names have an important connection with the server and thus considered as a key role in detecting and blocking the attacks. A research interest is how to distinguish the abnormal domain names when the characteristics of the domain name are difficult to represent and the abnormal domain names are changing all the time. In this research, we represent the features of domain names with the term frequency and inverse document frequency (TF-IDF) in feature vectors. Then we adopt a variational autoencoder (VAE) consisting of the encoder and decoder in order to compress the feature information. We convert the feature vectors to ones each of which consists of four values to represent domain names. At last, we use a quantum-enhanced support vector machine (QSVM) with a three-qubit system implemented to classify these domain names. We divide the dataset into a training set and a validation set. After training, we evaluate our scheme using 10-fold validation, and attain a test accuracy of 0.92.
Keyword (in Japanese) (See Japanese page) 
(in English) DNS / cybersecurity / variational autoencoder / support vector machine / quantum computing / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 275, SC2019-23, pp. 19-23, Nov. 2019.
Paper # SC2019-23 
Date of Issue 2019-11-01 (KBSE, SC) 
ISSN Online edition: ISSN 2432-6380
Copyright
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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)
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Conference Information
Committee KBSE SC  
Conference Date 2019-11-08 - 2019-11-09 
Place (in Japanese) (See Japanese page) 
Place (in English) Shinshu University 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SC 
Conference Code 2019-11-KBSE-SC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Malicious Domain Names Detection Based on TF-IDE and Variational Autoencoder: Classification with Quantum-enhanced Support Vector Machine 
Sub Title (in English)  
Keyword(1) DNS  
Keyword(2) cybersecurity  
Keyword(3) variational autoencoder  
Keyword(4) support vector machine  
Keyword(5) quantum computing  
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1st Author's Name Yuwei Sun  
1st Author's Affiliation The University of Tokyo (UTokyo)
2nd Author's Name Ng S. T. Chong  
2nd Author's Affiliation United Nations University (UNU)
3rd Author's Name Hideya Ochiai  
3rd Author's Affiliation The University of Tokyo (UTokyo)
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Speaker Author-1 
Date Time 2019-11-08 13:30:00 
Presentation Time 30 minutes 
Registration for SC 
Paper # KBSE2019-26, SC2019-23 
Volume (vol) vol.119 
Number (no) no.274(KBSE), no.275(SC) 
Page pp.19-23 
#Pages
Date of Issue 2019-11-01 (KBSE, SC) 


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