• DocumentCode
    187442
  • Title

    Using Invariants for Anomaly Detection: The Case Study of a SaaS Application

  • Author

    Frattini, Flavio ; Sarkar, Santonu ; Khasnabish, Jyotiska Nath ; Russo, S.

  • Author_Institution
    Univ. degli Studi di Napoli, Naples, Italy
  • fYear
    2014
  • fDate
    3-6 Nov. 2014
  • Firstpage
    383
  • Lastpage
    388
  • Abstract
    Invariants represent properties of a system that are expected to hold when everything goes well. Thus, the violation of an invariant most likely corresponds to the occurrence of an anomaly in the system. In this paper, we discuss the accuracy and the completeness of an anomaly detection system based on invariants. The case study we have taken is a back-end operation of a SaaS platform. Results show the rationality of the approach and discuss the impact of the invariant mining strategy on the detection capabilities, both in terms of accuracy and of time to reveal violations.
  • Keywords
    cloud computing; data mining; security of data; SaaS application; anomaly detection system; back-end operation; invariant mining strategy; system anomaly occurrence; system properties; Accuracy; Data mining; Detectors; Market research; Monitoring; Software as a service; Time series analysis; Anomaly detection; SaaS; invariants;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Software Reliability Engineering Workshops (ISSREW), 2014 IEEE International Symposium on
  • Conference_Location
    Naples
  • Type

    conf

  • DOI
    10.1109/ISSREW.2014.57
  • Filename
    6983871