A Hybrid Anomaly Detection Framework in Cloud Computing Using One-Class and Two-Class Support Vector Machines-Reference-Cited by-同舟云学术

A Hybrid Anomaly Detection Framework in Cloud Computing Using One-Class and Two-Class Support Vector Machines

Published:2012 Issue: Volume: Page:726-738
ISSN:0302-9743
Container-title:Advanced Data Mining and Applications
language:
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Author:

Fu Song,Liu Jianguo,Pannu Husanbir

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/978-3-642-35527-1_60.pdf

Reference43 articles.

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2. Belkin, M., Niyogi, P., Sindhwani, V.: Manifold Regularization: A geometric framework for learning from labeled and unlabeled examples. The Journal of Machine Learning Research 7, 2399–2434 (2006)

3. Ben-Hur, A., Horn, D., Siegelmann, H., Vapnik, V.: Support vector clustering. Journal of Machine Learning Research 2, 125–137 (2001)

4. Bishop, C.: Novelty detection and neural network validation. Vision, Image and Signal Processing 141(4), 217–222 (1994)

5. Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: A survey. ACM Computing Surveys 41(3), 15:1–15:58 (2009)

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