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Paper Abstract and Keywords
Presentation 2022-11-24 14:15
Research on Anomaly Detection through Analysis of Observed Traffic Using Self-Attention
Yuhang Zhou, Akihiro Nakao (UTokyo) NS2022-108
Abstract (in Japanese) (See Japanese page) 
(in English) Nowadays, threat activities have become an integral part of our network lives. The sophistication and variety of different types of cyberattacks are growing at an alarming rate, cyber-security has become a primary concern. The intrusion detection (ID) technique was made to deal with this problem, but traditional network intrusion detection system (NIDS) often fail to detect zero-day attacks, their capacity to swiftly respond to emerging intrusions is restricted. As a consequence, anomaly-based deep learning IDS is widely researched.
Though it has demonstrated exceptional ability in learning good representations from complex data, it suffers from a low recall rate, poor data efficiency, and speed-performance balancing issues.
This paper proposes a new structure for detecting anomalies using a self-attention-based model to leverage the time-series information among the packets to detect not only single point anomalies but also time-dependent anomalies, without relying on any time features given by the statistical system or suffering the long computation time, also improves the model’s generalization performance in the case of a lack of labeled data.
The experiment result shows the proposal is effective in improving recall rate than the traditional deep learning model up to 30% without suffering the long computation time.
Keyword (in Japanese) (See Japanese page) 
(in English) Anomaly Detection / Self-attention / Denoising Auto Encoder / Machine Learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 274, NS2022-108, pp. 47-52, Nov. 2022.
Paper # NS2022-108 
Date of Issue 2022-11-17 (NS) 
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 NS2022-108

Conference Information
Committee NS ICM CQ NV  
Conference Date 2022-11-24 - 2022-11-25 
Place (in Japanese) (See Japanese page) 
Place (in English) Humanities and Social Sciences Center, Fukuoka Univ. + Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Network quality, Network measurement/management, Network virtualization, Network service, Blockchain, Security, Network intelligence/AI, etc. 
Paper Information
Registration To NS 
Conference Code 2022-11-NS-ICM-CQ-NV 
Language English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Research on Anomaly Detection through Analysis of Observed Traffic Using Self-Attention 
Sub Title (in English)  
Keyword(1) Anomaly Detection  
Keyword(2) Self-attention  
Keyword(3) Denoising Auto Encoder  
Keyword(4) Machine Learning  
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1st Author's Name Yuhang Zhou  
1st Author's Affiliation The University of Tokyo (UTokyo)
2nd Author's Name Akihiro Nakao  
2nd Author's Affiliation The University of Tokyo (UTokyo)
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Speaker Author-1 
Date Time 2022-11-24 14:15:00 
Presentation Time 25 minutes 
Registration for NS 
Paper # NS2022-108 
Volume (vol) vol.122 
Number (no) no.274 
Page pp.47-52 
#Pages 6 
Date of Issue 2022-11-17 (NS) 

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