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Anomaly Detection in Cloud Based Application using System Calls

Aranitasi Marin, Neovius Mats, Anomaly Detection in Cloud Based Application using System Calls. In: Westphall Carlos Becker, Lee Yong Woo, Duncan Bob, Olmsted Aspen, Vassilakopoulos Michael, Lambrinoudakis Costas, Katsikas Sokratis K., Ege Raimund (Eds.), Cloud Computing 2017, 44–48, IARIA XPS, 2017.

Abstract:

Cloud computing is a rapidly developing computing paradigm. It enables dynamic on-demand resource distribution computing in a cost-effective manner. However, it introduces compelling concerns related to privacy and security of the data. As many of these have been extensively studied and are monitored effectively, this paper proposes a novel solution relying on detecting anomalies in system calls behavior of the system. We use Dempster-Shafer theory of evidence for learning the normality and show how to parametrize this in the method presented. The method is scalable to any set of system calls. Finally, we propose further challenges on this track.

BibTeX entry:

@INPROCEEDINGS{inpMaMa17a,
  title = {Anomaly Detection in Cloud Based Application using System Calls},
  booktitle = {Cloud Computing 2017},
  author = {Marin, Aranitasi and Mats, Neovius},
  editor = {Carlos Becker, Westphall and Yong Woo, Lee and Bob, Duncan and Aspen, Olmsted and Michael, Vassilakopoulos and Costas, Lambrinoudakis and Sokratis K., Katsikas and Raimund, Ege},
  publisher = {IARIA XPS},
  pages = {44–48},
  year = {2017},
  keywords = {Cloud computing; Security; System calls},
  ISSN = {2308-4294},
}

Belongs to TUCS Research Unit(s): Distributed Systems Laboratory (DS Lab)

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