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Erschienen in:
2012 | OriginalPaper | Buchkapitel
A Hybrid Anomaly Detection Framework in Cloud Computing Using One-Class and Two-Class Support Vector Machines
verfasst von : Song Fu, Jianguo Liu, Husanbir Pannu
Erschienen in: Advanced Data Mining and Applications
Verlag: Springer Berlin Heidelberg
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Modern production utility clouds contain thousands of computing and storage servers. Such a scale combined with ever-growing system complexity of their components and interactions, introduces a key challenge for anomaly detection and resource management for highly dependable cloud computing. Autonomic anomaly detection is a crucial technique for understanding emergent, cloud-wide phenomena and self-managing cloud resources for system level dependability assurance. We propose a new hybrid self-evolving anomaly detection framework using one-class and two-class support vector machines. Experimental results in an institute wide cloud computing system show that the detection accuracy of the algorithm improves as it evolves and it can achieve 92.1% detection sensitivity and 83.8% detection specificity, which makes it well suitable for building highly dependable clouds.