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An incremental anomaly detection model for virtual machines

Author

Listed:
  • Hancui Zhang
  • Shuyu Chen
  • Jun Liu
  • Zhen Zhou
  • Tianshu Wu

Abstract

Self-Organizing Map (SOM) algorithm as an unsupervised learning method has been applied in anomaly detection due to its capabilities of self-organizing and automatic anomaly prediction. However, because of the algorithm is initialized in random, it takes a long time to train a detection model. Besides, the Cloud platforms with large scale virtual machines are prone to performance anomalies due to their high dynamic and resource sharing characters, which makes the algorithm present a low accuracy and a low scalability. To address these problems, an Improved Incremental Self-Organizing Map (IISOM) model is proposed for anomaly detection of virtual machines. In this model, a heuristic-based initialization algorithm and a Weighted Euclidean Distance (WED) algorithm are introduced into SOM to speed up the training process and improve model quality. Meanwhile, a neighborhood-based searching algorithm is presented to accelerate the detection time by taking into account the large scale and high dynamic features of virtual machines on cloud platform. To demonstrate the effectiveness, experiments on a common benchmark KDD Cup dataset and a real dataset have been performed. Results suggest that IISOM has advantages in accuracy and convergence velocity of anomaly detection for virtual machines on cloud platform.

Suggested Citation

  • Hancui Zhang & Shuyu Chen & Jun Liu & Zhen Zhou & Tianshu Wu, 2017. "An incremental anomaly detection model for virtual machines," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-23, November.
    • Handle: RePEc:plo:pone00:0187488
    DOI: 10.1371/journal.pone.0187488
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    References listed on IDEAS

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    1. Jia Zhao & Liang Hu & Yan Ding & Gaochao Xu & Ming Hu, 2014. "A Heuristic Placement Selection of Live Virtual Machine Migration for Energy-Saving in Cloud Computing Environment," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-13, September.
    2. Andrey Rzhetsky & Tian Zheng & Chani Weinreb, 2006. "Self-Correcting Maps of Molecular Pathways," PLOS ONE, Public Library of Science, vol. 1(1), pages 1-8, December.
    3. Hugh P Shanahan & Anne M Owen & Andrew P Harrison, 2014. "Bioinformatics on the Cloud Computing Platform Azure," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-9, July.
    4. Tommaso Russo & Michele Scardi & Stefano Cataudella, 2014. "Applications of Self-Organizing Maps for Ecomorphological Investigations through Early Ontogeny of Fish," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-9, January.
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