A Light-weight Online Learning Framework for Network Traffic Abnormality Detection-Reference-Cited by-同舟云学术

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A Light-weight Online Learning Framework for Network Traffic Abnormality Detection

Published:2023-03 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Wireless Communications and Networking Conference (WCNC)
language:
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Author:

Wang Yitu¹,Dong Runqi¹,Nakachi Takayuki²,Wang Wei³

Affiliation:

1. North Minzu University,School of Electrical and Information Engineering,Yinchuan,China,750021

2. University of the Ryukyus,Information Technology Center,Nishihara-cho,Japan,9030213

3. Zhejiang University,College of Information Science and Electronic Engineering,Hangzhou,China,310027

Funder

North Minzu University

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10118584/10118587/10118849.pdf?arnumber=10118849

Reference28 articles.

1. Network traffic modeling for load prediction: a user-centric approach

2. Fast and compact per-flow traffic measurement through randomized counter sharing

3. Periodicity-Based Anomalies in Self-Similar Network Traffic Flow Measurements

4. A Reinforcement Learning-Based Network Traffic Prediction Mechanism in Intelligent Internet of Things

5. Providing public intradomain traffic matrices to the research community

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Network Traffic Anomaly Detection: A Revisiting to Gaussian Process and Sparse Representation;IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences;2024-01-01

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