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Real-Time Misbehavior Detection in IEEE 802.11TM: An Analytical Approach

  • Jin Tang
  • Yu Cheng
Chapter
  • 610 Downloads
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

In this chapter, we address the selfish misbehavior in the IEEE 802.11TM based wireless network. After a brief description on selfish misbehavior in 802.11TM, we first design a real-time backoff misbehavior detector, termed as the fair share detector (FS detector), which exploits the non-parametric cumulative sum (CUSUM) test to quickly find a selfish malicious node without any a priori knowledge of the statistics of the selfish misbehavior. We then develop a Markov chain based analytical model to systematically study the performance of the FS detector. Based on the analytical model, we can quantitatively compute the system configuration parameters for guaranteed performance in terms of average false positive rate, average detection delay and missed detection ratio under a detection delay constraint. We present simulation results to confirm the accuracy of our theoretical analysis as well as demonstrate the performance of the FS detector.

Keywords

Average Detection Delay Missed Detection Ratio Average False Positive Rate Selfish Misbehavior Malicious Nodes 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

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Copyright information

© The Author(s) 2013

Authors and Affiliations

  • Jin Tang
    • 1
  • Yu Cheng
    • 2
  1. 1.AT&T LabsWarrenvilleUSA
  2. 2.Department of Electrical and Computer EngineeringIllinois Institute of TechnologyChicagoUSA

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