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| Presentation | 2020-03-06 10:00 [Technology Exhibit] Machine learning pipeline for analyzing Large-scale traffic Morikawa Akira, Bo Hu (NTT)  NS2019-212 IN2019-103 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Recently, machine learning becomes promising to automatically extract intelligence from network traffic for cybersecurity, especially botnet detection. For detecting different types of components in a botnet such as scanning bots and its command and control servers, many researches have been proposed to preprocess data, generate traffic-based features, and build machine learning-based inference models, respectively. However, when applying multiple detection methods together to dive into details of the whole structure of a botnet, there may be many overlaps in those methods designed for different purposes such as bot and malicious server detection. In this study, we propose and develop a unified machine learning pipeline to enable diversified cybersecurity analysis. Moreover, we develop a graph-based tool to visualize analysis results. The proposed pipeline can aggregate traffic data preprocessing and generate traffic-based statistical features required in different analysis methods to enhance the computational efficiency, and therefore achieve various analysis on botnet. With the graph-based tool, we correlate the detected malicious hosts to clarify the whole picture of botnets. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | cyber security / botnet / machine learning / visualization / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 461, IN2019-103, pp. 151-151, March 2020. | |
| Paper # | IN2019-103 | |
| Date of Issue | 2020-02-27 (NS, IN) | |
| 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) | |
| Download PDF | NS2019-212 IN2019-103 |
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| Conference Information | |
| Committee | NS IN |
| Conference Date | 2020-03-05 - 2020-03-06 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Royal Hotel Okinawa Zanpa-Misaki |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | General |
| Paper Information | |
| Registration To | IN |
| Conference Code | 2020-03-NS-IN |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Machine learning pipeline for analyzing Large-scale traffic |
| Sub Title (in English) | |
| Keyword(1) | cyber security |
| Keyword(2) | botnet |
| Keyword(3) | machine learning |
| Keyword(4) | visualization |
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| 1st Author's Name | Morikawa Akira |
| 1st Author's Affiliation | NIPPON TELEGRAPH AND TELEPHONE CORPORATION (NTT) |
| 2nd Author's Name | Bo Hu |
| 2nd Author's Affiliation | NIPPON TELEGRAPH AND TELEPHONE CORPORATION (NTT) |
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| Speaker | Author-1 |
| Date Time | 2020-03-06 10:00:00 |
| Presentation Time | 300 minutes |
| Registration for | IN |
| Paper # | NS2019-212, IN2019-103 |
| Volume (vol) | vol.119 |
| Number (no) | no.460(NS), no.461(IN) |
| Page | p.191(NS), p.151(IN) |
| #Pages | 1 |
| Date of Issue | 2020-02-27 (NS, IN) |
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