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Paper Abstract and Keywords
Presentation 2010-11-04 15:00
[Poster Presentation] Anomaly Detection of Sequence Data Based on T-information
Ulrich Speidel (Univ. of Auckland), Stefan Jan Skudlarek, Hirosuke Yamamoto (Univ. of Tokyo) IBISML2010-76
Abstract (in Japanese) (See Japanese page) 
(in English) The application of anomaly detection to malfunction and misuse detection is an area of intense research, especially with respect to non-numerical sequence data, because of the increasing complexity of technical systems. Dependence on normal training data in this approach is a feature one would like to do without, thus spawning the problem of unsupervised anomaly detection. Data distance measures used for this problem are usually based on dot-product kernel functions. In this paper, however, we use T-information to define a distance-measure of two strings. T-information is a linearised measure derived from the T-complexity of a string, which in turn is the weighted number of steps taken by the T-decomposition string parsing algorithm based on the variable-length T-codes. We propose a simple clustering algorithm for unsupervised anomaly detection and validate the method using a network intrusion data set.
Keyword (in Japanese) (See Japanese page) 
(in English) sequence data / anomaly detection / T-information / T-complexity / clustering / data distance / similarity /  
Reference Info. IEICE Tech. Rep., vol. 110, no. 265, IBISML2010-76, pp. 127-134, Nov. 2010.
Paper # IBISML2010-76 
Date of Issue 2010-10-28 (IBISML) 
ISSN Print edition: ISSN 0913-5685    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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Conference Information
Committee IBISML  
Conference Date 2010-11-04 - 2010-11-06 
Place (in Japanese) (See Japanese page) 
Place (in English) IIS, Univ. of Tokyo 
Topics (in Japanese) (See Japanese page) 
Topics (in English) IBIS 2010 (Workshop on Information-based Induction Sciences) 
Paper Information
Registration To IBISML 
Conference Code 2010-11-IBISML 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Anomaly Detection of Sequence Data Based on T-information 
Sub Title (in English)  
Keyword(1) sequence data  
Keyword(2) anomaly detection  
Keyword(3) T-information  
Keyword(4) T-complexity  
Keyword(5) clustering  
Keyword(6) data distance  
Keyword(7) similarity  
Keyword(8)  
1st Author's Name Ulrich Speidel  
1st Author's Affiliation The University of Auckland (Univ. of Auckland)
2nd Author's Name Stefan Jan Skudlarek  
2nd Author's Affiliation The University of Tokyo (Univ. of Tokyo)
3rd Author's Name Hirosuke Yamamoto  
3rd Author's Affiliation The University of Tokyo (Univ. of Tokyo)
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Speaker Author-2 
Date Time 2010-11-04 15:00:00 
Presentation Time 180 minutes 
Registration for IBISML 
Paper # IBISML2010-76 
Volume (vol) vol.110 
Number (no) no.265 
Page pp.127-134 
#Pages 8 
Date of Issue 2010-10-28 (IBISML) 

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