Sliding window non-parametric cumulative sum: a quick algorithm to detect selfish behaviour in wireless networks

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Sliding window non-parametric cumulative sum: a quick algorithm to detect selfish behaviour in wireless networks

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When a node is not abiding by the rules of the protocol of a wireless network for its own benefit, it can cause severe degradation to network performance. Therefore it is important to detect such selfish behaviour. However, this is not an easy task. The main difficulty comes from the random operation of the carrier-sense multiple-access with collision avoidance (CSMA/CA) protocol, and is exacerbated by the nature of the wireless medium itself. The authors propose in this study a simple and quick algorithm, called sliding window non-parametric cumulative sum (SWN-CUSUM), to detect selfish nodes that deliberately modify its backoff window to gain unfair access to the network resources. SWN-CUSUM uses a sliding window to prevent unlimited build-up of the cumulating sum used in the protocol. The efficiency of this detection algorithm has been validated by extensive simulations using a Qualnet simulator. Comparative analysis of the proposed algorithm with a traditional CUSUM method demonstrates its superior performance with high detection accuracy and low false alarm rate. In addition, the authors compared SWN-CUSUM with other detection techniques, such as sequential probability ratio test and exponentially weighted moving average, the results show that our algorithm has a good performance in detection delay.

Inspec keywords: radio networks; telecommunication congestion control; protocols; probability; carrier sense multiple access

Other keywords: CSMA; exponentially weighted moving average; sequential probability ratio test; false alarm rate; collision avoidance; SWN-CUSUM; selfish behaviour detection; sliding window nonparametric cumulative sum; carrier-sense multiple-access; Qualnet simulator; wireless network protocol; network performance degradation; CA

Subjects: Multiple access communication; Other topics in statistics; Radio links and equipment; Protocols

References

    1. 1)
      • Scalable Network Technologies: ‘Qualnet simulator version 3.9’, www.scalable-networks.com.
    2. 2)
      • H.V. Poor . (1988) An introduction to signal detection and estimation.
    3. 3)
      • Toledo, A., Wang, X.: `A robust Kolmogorov-Smirnov detector for misbehavior in IEEE 802.11 DCF', Proc. IEEE Int. Conf. Communication, June 2007, Glasgow, UK, p. 1564–1569.
    4. 4)
      • Radosavac, S., Baras, J., Koutsopoulos, I.: `A framework for MAC protocol misbehavior detection in wireless networks', Proc. ACM Workshop Wireless Security, September 2005, Cologne, Germany, p. 33–42.
    5. 5)
    6. 6)
      • Michiardi, P., Molva, R.: `Game theoretic analysis of security in mobile and ad hoc networks', Technical report RR-02-070, April 2002.
    7. 7)
    8. 8)
      • Rong, Y., Lee, S., Choi, H.: `Detecting stations cheating on backoff rules in 802.11 networks using sequential analysis', Proc. IEEE INFOCOM, April 2006, Barcelona, Spain, p. 1–13.
    9. 9)
      • Kyasanur, P., Vaidya, N.: `Detection and handling of MAC layer misbehavior in wireless networks', Proc. Int. Conf. Dependable Systems and Networks, June 2003, San Francisco, USA, p. 173–182.
    10. 10)
    11. 11)
      • Wang, H., Zhang, D., Shin, K.: `Detecting SYN flooding attacks', Proc. INFOCOM, June 2002, NY, USA, p. 1530–1539.
    12. 12)
      • Chen, Y., Trappe, W., Martin, R.: `Detecting and localizing wireless spoofing attacks', Proc. IEEE SECON, October 2007, San Diego, USA, p. 193–202.
    13. 13)
      • M. Basseville , I.V. Nikiforov . (1993) Detection of abrupt changes: theory and application.
    14. 14)
      • Liu, C., Shu, Y., Yang, O., Li, M.: `A new mechanism to detect selfish behavior in IEEE 802.11 ad hoc networks', Proc. IEEE Int. Conf. Communication, June 2009, Dresden, Germany, p. 519–643.
    15. 15)
    16. 16)
      • ISO/IEC 8802-11:1999(E): ‘IEEE standard for wireless LAN medium access control (MAC) and physical layer (PHY) specifications’, August 1999.
    17. 17)
    18. 18)
      • Buchegger, S., Boudec, J.: `Performance analysis of the CONFIDANT protocol', Proc. MobiHoc, June 2002, Lausanne, Switzerland, p. 226–236.
    19. 19)
      • Bellardo, J., Savage, S.: `802.11 denial-of-service attacks: Real vulnerabilities and practical solutions', Proc. USENIX Security Symp., August 2003, Washington, DC, USA, p. 15–28.
    20. 20)
      • L. Li , J. Zhou , N. Xiao . (2007) DDoS attack detection algorithms based on entropy computing.
    21. 21)
      • Aad, I., Hubaux, J., Knightly, E.: `Denial of service resilience in ad hoc networks', Proc. ACM Mobicom, September 2004, PA, USA, p. 202–215.
    22. 22)
      • Wang, H., Zhang, D., Shin, K.: `SYN-dog: sniffing SYN flooding sources', Proc. 22nd IEEE Int. Conf. Distributed Computing Systems, July 2002, Vienna, Austria, p. 421–428.
    23. 23)
      • Cárdenas, A., Radosavac, S., Baras, J.: `Detection and prevention of MAC layer misbehavior in ad hoc networks', Proc. ACM SASN, October 2004, TX, USA, p. 17–22.
    24. 24)
      • Brik, V., Banerjee, S., Gruteser, M.: `Wireless device identification with radiometric signatures', Proc. Mobicom, September 2008, San Francisco, CA, USA, p. 116–127.
    25. 25)
      • B. Brodsky , B. Darkhovsky . (1993) Nonparametric methods in change point problems.
    26. 26)
      • Cagalj, M., Ganeriwal, S., Aad, I.: `On cheating in CSMA/CA ad hoc networks', Technical Report IC/2004/27, March 2004.
    27. 27)
    28. 28)
      • Ahmed, E., Clark, A., Mohay, G.: `A novel sliding window based change detection algorithm for asymmetric traffic', Proc. 2008 IFIP Int. Conf. Network and Parallel Computing, October 2008, Shanghai, China, p. 168–175.
    29. 29)
      • Pelechrinis, K., Yan, G., Eidenbenz, S.: `Detecting selfish exploitation of carrier sensing in 802.11 networks', Proc. IEEE INFOCOM, 2009, Rio De Janeiro, Brazil, p. 657–665.
    30. 30)
      • A. Wald . (1947) Sequential analysis.
    31. 31)
      • Faria, D., Cheriton, D.: `Detecting identity-based attacks in wireless networks using signalprints', Proc. ACM Workshop on Wireless Security, September 2006, Los Angeles, CA, USA, p. 43–52.
    32. 32)
    33. 33)
      • Gupta, V., Krishnamurthy, S., Faloutsous, M.: `Denial of service attacks at the MAC layer in wireless ad hoc networks', Proc. MILCOM, 2002, CA, USA, p. 1118–1123.
    34. 34)
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