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Paper Abstract and Keywords
Presentation 2017-03-13 15:15
Supervised Classification for Detecting Malware Infected Host in HTTP Traffic and Long-time Evaluation for Detection Performance using Mixed Data
Atsutoshi Kumagai, Yasushi Okano, Kazunori Kamiya, Masaki Tanikawa (NTT) ICSS2016-51
Abstract (in Japanese) (See Japanese page) 
(in English) The importance of post-infection countermeasures has greatly increased. Such countermeasures include generating blacklist based on communications made by malware. However, it is difficult for such methods to detect new type of communications made by sophisticated malware. In this paper, we propose a novel method for detecting malware-infected hosts by analyzing their communications based on machine learning. With the proposed method, logistic regression is used as classifiers, and features are extracted from HTTP traffic. The proposed method can eliminate the number of features while maintaining the detection performance by incorporating both sparse learning and feature summarization heuristics. In addition, we propose a novel evaluation procedure considering practical operation. Considering that actual malware-infected hosts generate not only malicious communications which are caused by malware but also normal communications which are caused by legitimate users, we mix malicious communications and normal communications for creating malicious testing data. Furthermore, we evaluate the long-time detection performance since it is important to detect malware-infected hosts correctly over a long period of time. The effectiveness of the proposed method is demonstrated with experiments using HTTP traffic data.
Keyword (in Japanese) (See Japanese page) 
(in English) machine learning / malware / malware-infected host / long-time evaluation for detection performance / mixed data / / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 522, ICSS2016-51, pp. 43-48, March 2017.
Paper # ICSS2016-51 
Date of Issue 2017-03-06 (ICSS) 
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 ICSS IPSJ-SPT  
Conference Date 2017-03-13 - 2017-03-14 
Place (in Japanese) (See Japanese page) 
Place (in English) University of Nagasaki 
Topics (in Japanese) (See Japanese page) 
Topics (in English) System Security, etc. 
Paper Information
Registration To ICSS 
Conference Code 2017-03-ICSS-SPT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Supervised Classification for Detecting Malware Infected Host in HTTP Traffic and Long-time Evaluation for Detection Performance using Mixed Data 
Sub Title (in English)  
Keyword(1) machine learning  
Keyword(2) malware  
Keyword(3) malware-infected host  
Keyword(4) long-time evaluation for detection performance  
Keyword(5) mixed data  
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1st Author's Name Atsutoshi Kumagai  
1st Author's Affiliation NTT Corporation (NTT)
2nd Author's Name Yasushi Okano  
2nd Author's Affiliation NTT Corporation (NTT)
3rd Author's Name Kazunori Kamiya  
3rd Author's Affiliation NTT Corporation (NTT)
4th Author's Name Masaki Tanikawa  
4th Author's Affiliation NTT Corporation (NTT)
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Speaker Author-1 
Date Time 2017-03-13 15:15:00 
Presentation Time 25 minutes 
Registration for ICSS 
Paper # ICSS2016-51 
Volume (vol) vol.116 
Number (no) no.522 
Page pp.43-48 
#Pages 6 
Date of Issue 2017-03-06 (ICSS) 

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