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Paper Abstract and Keywords
Presentation 2019-05-24 09:25
Feature Value for Low-Bandwidth L3, L4 DDoS Detection based on Number of 5-tuple Flows in 3-tuple Flow
Yuhei Hayashi (NTT), Hikofumi Suzuki (Shindai), Takeaki Nishioka (NTT) ICM2019-5
Abstract (in Japanese) (See Japanese page) 
(in English) Recently, new sophisticated attacks such as pulse-wave DDoS has been observed. The DDoS attack repeats short duration attacks, so the time-averaged bandwidth of the attack traffic can be observed as low rate. On the other hand, routers are already deployed in their network and it can send traffic flow information by using NetFlow etc. Level of DDoS countermeasure can be raised economically and quickly if the attacks can be detected by the flow information. Some researchers proposed to detect DDoS attack by calculating bandwidth from the flow information and collaborating it and machine learning. However, in a case where the bandwidth of attack is low so there is no significant difference between attack traffic and normal traffic in terms of bandwidth, the conventional approach is not effective.
To make up for the disadvantage of the conventional method, we propose a new feature value and its fast calculation method for detection low-bandwidth L3, L4 DDoS attacks. This feature value is based on a consideration that the number of 5-tuple flows existing in 3-tuple flow defined by (src_ip, dst_ip, dst_port) differs between normal traffic and attack traffic. In addition, we evaluated attack detection accuracy when our proposed feature value and Local Outline Filter (LOF) collaborate. Under the evaluation, we used the dataset obtained by carrying out attacks on the Shinshu University network. We also used the dataset obtained at the transit link of WIDE. The evaluation results show that the proposed feature value is effective to detect low-bandwidth L3, L4 attack while suppressing false negative and false positive
Keyword (in Japanese) (See Japanese page) 
(in English) DDoS / Detection / Low-bandwidth / NetFlow / sFlow / Machine Learning / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 52, ICM2019-5, pp. 65-70, May 2019.
Paper # ICM2019-5 
Date of Issue 2019-05-16 (ICM) 
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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Conference Information
Committee ICM IPSJ-CSEC IPSJ-IOT  
Conference Date 2019-05-23 - 2019-05-24 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To ICM 
Conference Code 2019-05-ICM-CSEC-IOT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Feature Value for Low-Bandwidth L3, L4 DDoS Detection based on Number of 5-tuple Flows in 3-tuple Flow 
Sub Title (in English)  
Keyword(1) DDoS  
Keyword(2) Detection  
Keyword(3) Low-bandwidth  
Keyword(4) NetFlow  
Keyword(5) sFlow  
Keyword(6) Machine Learning  
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Keyword(8)  
1st Author's Name Yuhei Hayashi  
1st Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
2nd Author's Name Hikofumi Suzuki  
2nd Author's Affiliation Shinshu University (Shindai)
3rd Author's Name Takeaki Nishioka  
3rd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
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Speaker Author-1 
Date Time 2019-05-24 09:25:00 
Presentation Time 25 minutes 
Registration for ICM 
Paper # ICM2019-5 
Volume (vol) vol.119 
Number (no) no.52 
Page pp.65-70 
#Pages 6 
Date of Issue 2019-05-16 (ICM) 

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