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 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) |
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KBSE2019-26 SC2019-23 |
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) |
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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) |
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DNS |
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cybersecurity |
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variational autoencoder |
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support vector machine |
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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 |
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The University of Tokyo (UTokyo) |
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Speaker |
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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 |
5 |
Date of Issue |
2019-11-01 (KBSE, SC) |
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