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 |
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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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) |
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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) |
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Keyword(1) |
Anomaly Detection |
Keyword(2) |
Traffic Pattern |
Keyword(3) |
Honeypot |
Keyword(4) |
Machine Learning |
Keyword(5) |
Attack Classification |
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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 |
#Pages |
6 |
Date of Issue |
2020-02-24 (ICM) |
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