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Paper Abstract and Keywords
Presentation 2019-07-04 09:00
Traffic Feature-based Botnet Detection Scheme Emphasizing the Importance of Long Patterns
Yichen An, Shuichiro Haruta, Sanghun Choi, Iwao Sasase (Keio Univ.) CS2019-18
Abstract (in Japanese) (See Japanese page) 
(in English) The botnet detection is imperative. Among several detection schemes, the promising one uses the communication sequences. The main idea of that scheme is that the communication sequences represent special feature since they are controlled by programs. That sequence is tokenized to truncated sequences by $n$-gram and the numbers of each pattern's occurrence are used as a feature vector. However, although the features are normalized by the total number of all patterns' occurrences, the number of occurrences in larger $n$ are less than those of smaller $n$. That is, regardless of the value of $n$, the previous scheme normalizes it by the total number of all patterns' occurrences. As a result, normalized long patterns' features become very small value and are hidden by others. In order to overcome this shortcoming, in this paper, we propose tit. We realize the emphasizing by two ideas. The first idea is normalizing occurrences by the total number of occurrences in each $n$ instead of the total number of all patterns' occurrences. By doing this, smaller occurrences in larger $n$ are normalized by smaller values and the feature becomes more balanced with larger value. The second idea is giving weights to the normalized features by calculating ranks of the normalized feature. By weighting features according to the ranks, we can get more outstanding features of longer patterns. By the computer simulation with real dataset, we show the effectiveness of our scheme.
Keyword (in Japanese) (See Japanese page) 
(in English) botnet detection / machine learning / feature emphasizing / / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 101, CS2019-18, pp. 31-35, July 2019.
Paper # CS2019-18 
Date of Issue 2019-06-27 (CS) 
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 CS  
Conference Date 2019-07-04 - 2019-07-05 
Place (in Japanese) (See Japanese page) 
Place (in English) Amami City Social Welfare Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Next Generation Networks, Access Networks, Broadband Access, Power Line Communications, Wireless Communication Systems, Coding Systems, etc. 
Paper Information
Registration To CS 
Conference Code 2019-07-CS 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Traffic Feature-based Botnet Detection Scheme Emphasizing the Importance of Long Patterns 
Sub Title (in English)  
Keyword(1) botnet detection  
Keyword(2) machine learning  
Keyword(3) feature emphasizing  
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1st Author's Name Yichen An  
1st Author's Affiliation Keio University (Keio Univ.)
2nd Author's Name Shuichiro Haruta  
2nd Author's Affiliation Keio University (Keio Univ.)
3rd Author's Name Sanghun Choi  
3rd Author's Affiliation Keio University (Keio Univ.)
4th Author's Name Iwao Sasase  
4th Author's Affiliation Keio University (Keio Univ.)
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Speaker Author-1 
Date Time 2019-07-04 09:00:00 
Presentation Time 15 minutes 
Registration for CS 
Paper # CS2019-18 
Volume (vol) vol.119 
Number (no) no.101 
Page pp.31-35 
#Pages
Date of Issue 2019-06-27 (CS) 


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