Anomaly Detection in Cloud Based Application using System Calls

Marin Aranitasi, Mats Neovius

    Research output: Chapter in Book/Conference proceedingConference contributionScientificpeer-review

    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.

    Original languageUndefined/Unknown
    Title of host publicationCLOUD COMPUTING 2017 The Eighth International Conference on Cloud Computing, GRIDs, and Virtualization
    EditorsWestphall Carlos Becker, Lee Yong Woo, Duncan Bob, Olmsted Aspen, Vassilakopoulos Michael, Lambrinoudakis Costas, Katsikas Sokratis K., Ege Raimund
    PublisherIaria xps press
    Pages44–48
    ISBN (Print)978-1-61208-529-6
    Publication statusPublished - 2017
    MoE publication typeA4 Article in a conference publication
    EventInternational Conference on Cloud Computing, GRIDs, and Virtualization - Eighth International Conference on Cloud Computing, GRIDs, and Virtualization
    Duration: 19 Feb 201723 Feb 2017

    Conference

    ConferenceInternational Conference on Cloud Computing, GRIDs, and Virtualization
    Period19/02/1723/02/17

    Keywords

    • Cloud computing
    • Information Security
    • Kernel methods

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