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Paper Abstract and Keywords
Presentation 2021-11-17 09:00
Analysis of Behaviors in Campus Network based on Traffic Patterns at Network Switches
Yuya Nakanishi, Shingo Ata (Osaka City Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) In recent growth of ICT, especially in Universities, the enhancement of education by ICT such as remote lectures and the increase of BYODs (Bring Your Own Devices) of students, leads the importance of campus network as infrastructure of educational and/or research activities. On the other hand, the spread of BYODs, which are not suitable to be contralled centrally, may degrade safety and stability of network bahavior due to anomally traffic from such devices. It is therefore important to realize a detection of anomalies at the very early stage. However, to follow the behavior of campus network accurately, it is necessary to collect huge log data from network switches, so a light-weight method to detect the event of anomalies is promising for real-time detection. In this study we consider a method to detect anomalies based on traffic patterns of network switches using a learning-based approach, and perform a feasilibity through numerical evaluations.
Keyword (in Japanese) (See Japanese page) 
(in English) Anomaly Detection / Machine Learning / Network Traffic / Campus Network / Traffic Pattern / / /  
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Conference Information
Committee RISING  
Conference Date 2021-11-15 - 2021-11-17 
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Paper Information
Registration To RISING 
Conference Code 2021-11-RISING 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Analysis of Behaviors in Campus Network based on Traffic Patterns at Network Switches 
Sub Title (in English)  
Keyword(1) Anomaly Detection  
Keyword(2) Machine Learning  
Keyword(3) Network Traffic  
Keyword(4) Campus Network  
Keyword(5) Traffic Pattern  
1st Author's Name Yuya Nakanishi  
1st Author's Affiliation Osaka City University (Osaka City Univ.)
2nd Author's Name Shingo Ata  
2nd Author's Affiliation Osaka City University (Osaka City Univ.)
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Speaker Author-1 
Date Time 2021-11-17 09:00:00 
Presentation Time 50 minutes 
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