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Paper Abstract and Keywords
Presentation 2023-03-02 14:40
Feature Selection Method for Predicting Network Failures on CNF 5GC Using Machine Learning with Low Layer Log Data
Takeru Hakii, Norihiro Fukumoto, Akihiro Nakao (UTokyo) NS2022-192
Abstract (in Japanese) (See Japanese page) 
(in English) In complex networks, once a failure occurs, it takes a long time to identify and recover from the cause of the failure, and the failure can be large enough to have a huge impact on society as a whole. However, storing all eBPF metrics and using them to train models increases the processing load and resource consumption of the models, which negatively affects performance and development efficiency. Therefore, we propose a feature selection method based on outlier processing using the ratio of the mean values under fault and normal conditions. The proposed method reduces the number of metrics from 3,325 to 320. We train a model that predicts whether a failure has occurred after 600 seconds of simulation using the proposed method, and compare its F1 score with that of a model that does not use the proposed method. The results show that the model with the proposed method predicts an F1 score of 0.93 at 140 seconds into the simulation, and an F1 score of 0.90 at 130 seconds. These results exceed those of the model without the proposed method. Therefore, our proposed method is not only effective in reducing the computational complexity by reducing the size of the model, but also in improving the accuracy and speed of prediction.
Keyword (in Japanese) (See Japanese page) 
(in English) 5G / CNF / core network / machine learning / failure prediction / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 406, NS2022-192, pp. 145-150, March 2023.
Paper # NS2022-192 
Date of Issue 2023-02-23 (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 NS2022-192

Conference Information
Committee IN NS  
Conference Date 2023-03-02 - 2023-03-03 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Convention Centre + Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General 
Paper Information
Registration To NS 
Conference Code 2023-03-IN-NS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Feature Selection Method for Predicting Network Failures on CNF 5GC Using Machine Learning with Low Layer Log Data 
Sub Title (in English)  
Keyword(1) 5G  
Keyword(2) CNF  
Keyword(3) core network  
Keyword(4) machine learning  
Keyword(5) failure prediction  
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1st Author's Name Takeru Hakii  
1st Author's Affiliation The University of Tokyo (UTokyo)
2nd Author's Name Norihiro Fukumoto  
2nd Author's Affiliation The University of Tokyo (UTokyo)
3rd Author's Name Akihiro Nakao  
3rd Author's Affiliation The University of Tokyo (UTokyo)
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Speaker Author-1 
Date Time 2023-03-02 14:40:00 
Presentation Time 20 minutes 
Registration for NS 
Paper # NS2022-192 
Volume (vol) vol.122 
Number (no) no.406 
Page pp.145-150 
#Pages 6 
Date of Issue 2023-02-23 (NS) 

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