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Paper Abstract and Keywords
Presentation 2023-11-22 16:25
Improving the accuracy of flow prediction and anomaly detection in GAMPAL, a general-purpose anomaly detection mechanism for Internet traffic
Taku Wakui (Keio Univ./Hitachi), Fumio Teraoka (Keio Univ.), Takao Kondo (Hokkaido Univ./Keio Univ.) IA2023-41
Abstract (in Japanese) (See Japanese page) 
(in English) The authors propose a general-purpose anomaly detection mechanism using Prefix Aggregate without Labeled data (GAMPAL) for Internet traffic. GAMPAL aggregates flows based on BGP (Border Gateway Protocol), and detects anomalies by comparing the predicted traffic flow for each aggregated group with the observed traffic flow. The model of the existing method is trained with time-series data of past flows as input and future values as output. In order to improve the accuracy of prediction and detection, this paper uses the flow rate as input and time-based features such as month, hour, minute, and day of the week, which are generated from the recorded date and time of each value, as output. This method enables to learn various temporal characteristics, such as weekly and daily. The evaluation results show that RFR (Random Forest Regressor) is the most suitable for the proposed learning method among LSTM-RNN (Long Short-Term Memory Recurrent Neural Network), RFR, and SVM (Support Vector Machine). The difference between predicted and observed flow size is reduced by 80.8% from our previous method.
Computation time is also reduced. Furthermore, when connection failure for YouTube was occurring, the observed values are continuously much lower than the predicted values. It shows this method can detects anomalies.
Keyword (in Japanese) (See Japanese page) 
(in English) Network Traffic Analysis / Anomaly Detection / Internet Backbone / Machine Learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 277, IA2023-41, pp. 33-40, Nov. 2023.
Paper # IA2023-41 
Date of Issue 2023-11-15 (IA) 
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 IA2023-41

Conference Information
Committee IA  
Conference Date 2023-11-22 - 2023-11-22 
Place (in Japanese) (See Japanese page) 
Place (in English) Aomori Prefecture Tourist Center ASPM (Aomori) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Student Sessions, etc. (cosponsored by Committee on Internet Technology) 
Paper Information
Registration To IA 
Conference Code 2023-11-IA 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Improving the accuracy of flow prediction and anomaly detection in GAMPAL, a general-purpose anomaly detection mechanism for Internet traffic 
Sub Title (in English)  
Keyword(1) Network Traffic Analysis  
Keyword(2) Anomaly Detection  
Keyword(3) Internet Backbone  
Keyword(4) Machine Learning  
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1st Author's Name Taku Wakui  
1st Author's Affiliation Graduate School of Keio University/Hitachi, Ltd. (Keio Univ./Hitachi)
2nd Author's Name Fumio Teraoka  
2nd Author's Affiliation Keio University (Keio Univ.)
3rd Author's Name Takao Kondo  
3rd Author's Affiliation Hokkaido University/Keio University (Hokkaido Univ./Keio Univ.)
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Speaker Author-1 
Date Time 2023-11-22 16:25:00 
Presentation Time 25 minutes 
Registration for IA 
Paper # IA2023-41 
Volume (vol) vol.123 
Number (no) no.277 
Page pp.33-40 
#Pages 8 
Date of Issue 2023-11-15 (IA) 

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