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Paper Abstract and Keywords
Presentation 2024-02-29 11:10
An unsupervised online learning-based traffic classification and anomaly detection method for 5G-IIoT systems
Yuxuan Shi, Qianqian Pan, Akihiro Nakao (U Tokyo) NS2023-188
Abstract (in Japanese) (See Japanese page) 
(in English) In the context of Society 5.0, the evolution of the Internet of Things (IoT) and its ever growing demands of massive Machine Type Communications(mMTC) and Ultra Reliable Low Latency Communications(URLLC), have brought congestion-related challenges to the IoT systems. Particularly, the congestion in large-scale Industrial IoT (IIoT) caused by attacks or malfunctions, is threatening the reliability of large scale IIoT systems and the traditional methods of manual congestion response. In order to enhance the robustness of 5G-IIoT systems, there is an increasing demand for AI-based automatic anomaly detection and congestion prevention methods.
The aim of this study is to develop an autonomous system for local 5G IIoT networks, capable of identifying anomaly traffic patterns based on data from the Data plane(D-Plane), and isolating the anomaly device(s) to another gNB using the RAN Intelligence Controller(RIC) during an congestion event. First, an unsupervised online learning-based traffic classification and anomaly detection method is designed based on decrypted D-Plane traffic. Second, a RIC-enabled congestion control and mitigation scheme is proposed to isolate the anomaly devices to another gNB. Third, we establish a virtual local 5G environment testbed to generate Message Queuing Telemetry Transport(MQTT)-IIoT communication traffic to evaluate the performance of the proposed method. Compared with conventional methods, our proposed method improves the overall classification accuracy from 86.9% to 99.7%.
Keyword (in Japanese) (See Japanese page) 
(in English) Local 5G / RIC / Industrial IoT(IIoT) / device classification / anomaly detection / unsupervised machine learning / online machine learning /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 397, NS2023-188, pp. 96-102, Feb. 2024.
Paper # NS2023-188 
Date of Issue 2024-02-22 (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 NS2023-188

Conference Information
Committee NS IN  
Conference Date 2024-02-29 - 2024-03-01 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Convention Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General 
Paper Information
Registration To NS 
Conference Code 2024-02-NS-IN 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) An unsupervised online learning-based traffic classification and anomaly detection method for 5G-IIoT systems 
Sub Title (in English)  
Keyword(1) Local 5G  
Keyword(2) RIC  
Keyword(3) Industrial IoT(IIoT)  
Keyword(4) device classification  
Keyword(5) anomaly detection  
Keyword(6) unsupervised machine learning  
Keyword(7) online machine learning  
Keyword(8)  
1st Author's Name Yuxuan Shi  
1st Author's Affiliation The University of Tokyo (U Tokyo)
2nd Author's Name Qianqian Pan  
2nd Author's Affiliation The University of Tokyo (U Tokyo)
3rd Author's Name Akihiro Nakao  
3rd Author's Affiliation The University of Tokyo (U Tokyo)
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Speaker Author-1 
Date Time 2024-02-29 11:10:00 
Presentation Time 25 minutes 
Registration for NS 
Paper # NS2023-188 
Volume (vol) vol.123 
Number (no) no.397 
Page pp.96-102 
#Pages 7 
Date of Issue 2024-02-22 (NS) 

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