• DocumentCode
    3435298
  • Title

    Anomaly Detection in IaaS Clouds

  • Author

    Doelitzscher, Frank ; Knahl, Martin ; Reich, Christoph ; Clarke, N.

  • Author_Institution
    Cloud Res. Lab., Furtwangen Univ., Furtwangen, Germany
  • Volume
    1
  • fYear
    2013
  • fDate
    2-5 Dec. 2013
  • Firstpage
    387
  • Lastpage
    394
  • Abstract
    Security is still a major concern in Cloud computing, especially the detection of nefarious use or abuse of cloud instances. One reason for this, is the ever-growing complexity and dynamic of the underlying system design and architecture. To be able to detect misuse of cloud instances, this work presents an anomaly detection system for Infrastructure as a Service Clouds. It is based on Cloud customers´ usage behaviour analysis. Neural networks are used to analyse and learn the normal usage behaviour of Cloud customers, to then detect anomalies which could originate from a cloud security incident caused by an overtaken virtual machine. It increases transparency for Cloud customers about the security of their Cloud instances and supports the Cloud provider to detect misuse of their infrastructure. A simulation environment and an anomaly detection prototype get presented. Experiments validate the effectiveness of the proposed system.
  • Keywords
    cloud computing; neural nets; security of data; IaaS clouds; anomaly detection; cloud computing; cloud customer usage behaviour analysis; cloud provider; cloud security incident; infrastructure as a service; nefarious use detection; neural networks; overtaken virtual machine; system architecture; system design; Biological neural networks; Cloud computing; Mathematical model; Monitoring; Security; Training; Virtual machining; Cloud Computing; Cloud Security; Machine Learning; Neural Networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cloud Computing Technology and Science (CloudCom), 2013 IEEE 5th International Conference on
  • Conference_Location
    Bristol
  • Type

    conf

  • DOI
    10.1109/CloudCom.2013.57
  • Filename
    6753822