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Paper Abstract and Keywords
Presentation 2010-03-05 11:00
Unsupervised Ensemble Anomaly Detection Method using Time-Periodical Packet Sampling
Shuichi Nawata, Masato Uchida (Kyushu Inst. of Tech.), Yu Gu (NEC Labs America), Masato Tsuru, Yuji Oie (Kyushu Inst. of Tech.) IN2009-198
Abstract (in Japanese) (See Japanese page) 
(in English) We propose an anomaly detection method that trains a baseline model describing the normal behavior of network traffic without using manually labeled traffic data. The trained baseline distribution is used as the basis for comparison with the audit network traffic. The proposed method can be carried out in an unsupervised manner through the use of time-periodical packet sampling for a different purpose from which it was intended. That is, we take advantage of the lossy nature of packet sampling for the purpose of extracting normal packets from the unlabeled original traffic data. By using real traffic traces, we show that the proposed method is comparable to the conventional method that requires manually labeled traffic data to train the baseline model. In addition, in order to mitigate the possible performance variation due to probabilistic nature of sampled traffic data, we devised an ensemble anomaly detection method that exploits multiple baseline models in parallel. Experimental results show that the proposed ensemble anomaly detection performs well and is not affected by the variability of time-periodical packet sampling.
Keyword (in Japanese) (See Japanese page) 
(in English) Time-Periodical Packet Sampling / Unsupervised Anomaly Detection / Ensemble Learning / / / / /  
Reference Info. IEICE Tech. Rep., vol. 109, no. 449, IN2009-198, pp. 325-330, March 2010.
Paper # IN2009-198 
Date of Issue 2010-02-25 (IN) 
ISSN Print edition: ISSN 0913-5685    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 NS IN  
Conference Date 2010-03-04 - 2010-03-05 
Place (in Japanese) (See Japanese page) 
Place (in English) Miyazaki Phoenix Seagaia Resort (Miyazaki) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General, NS+IN workshop (March 4-5) 
Paper Information
Registration To IN 
Conference Code 2010-03-NS-IN 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Unsupervised Ensemble Anomaly Detection Method using Time-Periodical Packet Sampling 
Sub Title (in English)  
Keyword(1) Time-Periodical Packet Sampling  
Keyword(2) Unsupervised Anomaly Detection  
Keyword(3) Ensemble Learning  
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1st Author's Name Shuichi Nawata  
1st Author's Affiliation Kyushu Institute of Technology (Kyushu Inst. of Tech.)
2nd Author's Name Masato Uchida  
2nd Author's Affiliation Kyushu Institute of Technology (Kyushu Inst. of Tech.)
3rd Author's Name Yu Gu  
3rd Author's Affiliation NEC Laboratories America (NEC Labs America)
4th Author's Name Masato Tsuru  
4th Author's Affiliation Kyushu Institute of Technology (Kyushu Inst. of Tech.)
5th Author's Name Yuji Oie  
5th Author's Affiliation Kyushu Institute of Technology (Kyushu Inst. of Tech.)
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Speaker Author-1 
Date Time 2010-03-05 11:00:00 
Presentation Time 20 minutes 
Registration for IN 
Paper # IN2009-198 
Volume (vol) vol.109 
Number (no) no.449 
Page pp.325-330 
#Pages
Date of Issue 2010-02-25 (IN) 


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