Traffic Anomaly Detection Based on Robust Principal Component Analysis Using Periodic Traffic Behavior-Reference-Cited by-同舟云学术

Traffic Anomaly Detection Based on Robust Principal Component Analysis Using Periodic Traffic Behavior

Published:2017 Issue:5 Volume:E100.B Page:749-761
ISSN:0916-8516
Container-title:IEICE Transactions on Communications
language:en
Short-container-title:IEICE Trans. Commun.

Author:

MATSUDA Takahiro¹,MORITA Tatsuya¹,KUDO Takanori²,TAKINE Tetsuya¹

Affiliation:

1. Graduate School of Engineering, Osaka University

2. Faculty of Science and Engineering, Setsunan University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Software

Link

https://www.jstage.jst.go.jp/article/transcom/E100.B/5/E100.B_2016EBP3239/_pdf

Reference32 articles.

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3. [3] O. Argon, Y. Shavitt, and U. Weinsberg, “Inferring the periodicity in large-scale Internet measurements,” Proc. IEEE INFOCOM 2013, pp.1672-1680, April 2013.

4. [4] C.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.

5. [5] D. Brauckhoff, K. Salamatian, and M. May, “Applying PCA for traffic anomaly detection: Problems and solutions,” Proc. The 28th IEEE Conference on Computer Communications (INFOCOM 2009), pp.2866-2870, April 2009.

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