• DocumentCode
    1304480
  • Title

    Afriat´s Test for Detecting Malicious Agents

  • Author

    Krishnamurthy, Vikram ; Hoiles, William

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Univ. of British Columbia, Vancouver, BC, Canada
  • Volume
    19
  • Issue
    12
  • fYear
    2012
  • Firstpage
    801
  • Lastpage
    804
  • Abstract
    How can one detect if a set of agents is deliberately trying to avoid being detected? By assuming malicious agents are utility maximizers, we use a remarkable result developed by Afriat to construct a decision test that identifies such malicious agents with pre-specified Type-I error probability. Also a stochastic gradient algorithm is given to adapt the probe signals in real time to minimize the Type-II error probabilities of the decision test.
  • Keywords
    security of data; Afriat test; decision test; malicious agents detection; prespecifíed type-I error probability; probe signals; stochastic gradient algorithm; type-II error probabilities; Noise measurement; Optimization; Probes; Quality of service; Sensors; Signal processing algorithms; Vectors; Afriat´s Theorem; anomaly detection; networks; stochastic gradient algorithm; utility maximization;
  • fLanguage
    English
  • Journal_Title
    Signal Processing Letters, IEEE
  • Publisher
    ieee
  • ISSN
    1070-9908
  • Type

    jour

  • DOI
    10.1109/LSP.2012.2221711
  • Filename
    6319355