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Paper Abstract and Keywords
Presentation 2020-03-03 10:00
Automatic Accumulation of Learning Data on Learning-based Anomaly Detection Utilizing Communication Traffics
Natsuki Fukazawa, Naoki Yoshida, Shingo Ata, Ikuo Oka (Osaka City Univ.) ICM2019-50
Abstract (in Japanese) (See Japanese page) 
(in English) With the advancement and diversification of information infrastructure in recent years, the importance
of network security is becoming much critical. Network-based Intrusion Detection System (NIDS) is one of important
security systems which constantly monitors communication traffic and detects potentially malicious communication.
There have been studies on the adaptation of machine learning (ML) for anomaly detection. An important issue
on these ML-based algorithms is how to collect a good training data for achieving high accuracy of detection.
Especially, automatic way to accumulate training data is still challenging in order to follow unexpected or unknown
anomalies in future. In this paper, we propose a method to create training data automatically by analyzing the
correlation with statistics of network traffic and log data of events collected by a honeypot, which collects behavior
of attacks by injecting known vulnerabilities intentionally. Numerical evaluations show that we can detect similar
anomalies by only monitoring traffic statistics with training data accumulated by our method.
Keyword (in Japanese) (See Japanese page) 
(in English) Anomaly Detection / Traffic Pattern / Honeypot / Machine Learning / Attack Classification / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 438, ICM2019-50, pp. 49-54, March 2020.
Paper # ICM2019-50 
Date of Issue 2020-02-24 (ICM) 
ISSN Online edition: ISSN 2432-6380
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)
Download PDF ICM2019-50

Conference Information
Committee ICM  
Conference Date 2020-03-02 - 2020-03-03 
Place (in Japanese) (See Japanese page) 
Place (in English) Ohama Nobumoto Memorial Hall 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To ICM 
Conference Code 2020-03-ICM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Automatic Accumulation of Learning Data on Learning-based Anomaly Detection Utilizing Communication Traffics 
Sub Title (in English)  
Keyword(1) Anomaly Detection  
Keyword(2) Traffic Pattern  
Keyword(3) Honeypot  
Keyword(4) Machine Learning  
Keyword(5) Attack Classification  
1st Author's Name Natsuki Fukazawa  
1st Author's Affiliation Osaka City University (Osaka City Univ.)
2nd Author's Name Naoki Yoshida  
2nd Author's Affiliation Osaka City University (Osaka City Univ.)
3rd Author's Name Shingo Ata  
3rd Author's Affiliation Osaka City University (Osaka City Univ.)
4th Author's Name Ikuo Oka  
4th Author's Affiliation Osaka City University (Osaka City Univ.)
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Speaker Author-1 
Date Time 2020-03-03 10:00:00 
Presentation Time 20 minutes 
Registration for ICM 
Paper # ICM2019-50 
Volume (vol) vol.119 
Number (no) no.438 
Page pp.49-54 
Date of Issue 2020-02-24 (ICM) 

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