Nothing Special   »   [go: up one dir, main page]

Skip to main content
Log in

A Green Approach for Selfish Misbehavior Detection in 802.11-Based Wireless Networks

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

IEEE 802.11 is one of the most well-established and widely used standard for wireless LAN. Its Medium Access control (MAC) layer assumes that the devices adhere to the standard’s rules and timers to assure fair access and sharing of the medium. However, wireless cards driver flexibility and configurability make it possible for selfish misbehaving nodes to take advantages over the other well-behaving nodes. The existence of selfish nodes degrades the QoS for the other devices in the network and may increase their energy consumption. In this paper we propose a green solution for selfish misbehavior detection in IEEE 802.11-based wireless networks. The proposed scheme works in two phases: Global phase which detects whether the network contains selfish nodes or not, and Local phase which identifies which node or nodes within the network are selfish. Usually, the network must be frequently examined for selfish nodes during its operation since any node may act selfishly. Our solution is green in the sense that it saves the network resources as it avoids wasting the nodes energy by examining all the individual nodes of being selfish when it is not necessary. The proposed detection algorithm is evaluated using extensive OPNET simulations. The results show that the Global network metric clearly indicates the existence of a selfish node while the Local nodes metric successfully identified the selfish node(s). We also provide mathematical analysis for the selfish misbehaving and derived formulas for the successful channel access probability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Ieee std 802.11g-2003 (1997). http://standards.ieee.org/findstds/standard/802.11g-2003.html

  2. Bianchi G (2000) Performance analysis of the ieee 802.11 distributed coordination function. IEEE J Sel Areas Commun 18(3):535–547. doi:10.1109/49.840210

    Article  Google Scholar 

  3. Cagalj M, Ganeriwal S, Aad I, Hubaux JP (2004) On cheating in csma/ca ad hoc networks. Tech. rep., Proc. IEEE INFOCOM 2005

  4. Cardenas A, Radosavac S, Baras J (2009) Evaluation of detection algorithms for mac layer misbehavior: Theory and experiments. IEEE/ACM Trans Networking 17(2):605–617

    Article  Google Scholar 

  5. Choi J, Min A, Shin K (2011) A lightweight passive online detection method for pinpointing misbehavior in wlans. IEEE Trans Mob Comput 10(12):1681–1693. doi:10.1109/TMC.2010.262

    Article  Google Scholar 

  6. Deng DJ, Chen HC, Chao HC, Huang YM (2011) A collision alleviation scheme for ieee 802.11 p vanets. Wirel Pers Commun 56(3):371–383

    Article  Google Scholar 

  7. Deng DJ, Ke CH, Chao HC, Huang YM (2010) On delay constrained cac scheme and scheduling policy for cbr traffic in ieee 802.11 e wireless lans. Wirel Commun Mob Comput 10(11):1509–1520

    Article  Google Scholar 

  8. Hayajneh T, Almashaqbeh G, Ullah S, Vasilakos A (2014) A survey of wireless technologies coexistence in wban: analysis and open research issues. Wirel Netw 20(8):2165–2199. doi:10.1007/s11276-014-0736-8

  9. Hayajneh T, Krishnamurthy P, Tipper D, Kim T (2009) Detecting malicious packet dropping in the presence of collisions and channel errors in wireless ad hoc networks. In: IEEE international conference on communications, 2009. ICC ’09, pp. 1–6. doi:10.1109/ICC.2009.5198910

  10. Kim H, Kim H, Park W, Bae M (2013) Disabling misbehavior with traffic constraints in wlans. The Computer Journal. doi:10.1093/comjnl/bxt121

  11. Konorski J (2001) Protection of fairness for multimedia traffic streams in a non-cooperative wireless lan setting. In: van Sinderen M, Nieuwenhuis L (eds) Protocols for Multimedia Systems. Lecture Notes in Computer Science, vol 2213. Springer, Berlin Heidelberg, pp 116–129. doi:10.1007/3-540-45481-0_10

  12. Konorski J (2002) Multiple access in ad-hoc wireless lans with noncooperative stations. In: Gregori E, Conti M, Campbell A, Omidyar G, Zukerman M (eds) NETWORKING 2002: Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile and Wireless Communications, Lecture Notes in Computer Science vol. 2345, pp. 1141–11466. doi:10.1007/3-540-47906-6_98

  13. Kyasanur P, Vaidya N (2003) Detection and handling of mac layer misbehavior in wireless networks. In: Proceedings of the 2003 international conference on dependable systems and networks, 2003. pp 173–182. doi:10.1109/DSN.2003.1209928

  14. Kyasanur P, Vaidya N (2005) Selfish mac layer misbehavior in wireless networks. IEEE Trans Mob Comput 4(5):502–516. doi:10.1109/TMC.2005.71

    Article  Google Scholar 

  15. Li M, Salinas S, Li P, Sun J, Huang X (2014) Mac-layer selfish misbehavior in ieee 802.11 ad hoc networks: Detection and defense. IEEE Trans Mob Comput PP(99):1–1. doi:10.1109/TMC.2014.2348560

    Google Scholar 

  16. Lopez Toledo A, Wang X (2007) A robust kolmogorov-smirnov detector for misbehavior in ieee 802.11 dcf. In: IEEE international conference on communications, 2007, ICC ’07, pp 1564–1569. doi:10.1109/ICC.2007.262

  17. Paul U, Kashyap A, Maheshwari R, Das S (2013) Passive measurement of interference in wifi networks with application in misbehavior detection. IEEE Trans Mob Comput 12(3):434–446. doi:10.1109/TMC.2011.259

    Article  Google Scholar 

  18. Pelechrinis K, Yan G, Eidenbenz S, Krishnamurthy S (2009) Detecting selfish exploitation of carrier sensing in 802.11 networks. In: INFOCOM 2009, IEEE, pp 657–665

  19. Pelechrinis K, Yan G, Eidenbenz S, Krishnamurthy S (2012) Detection of selfish manipulation of carrier sensing in 802.11 networks. IEEE Trans Mob Comput 11(7):1086–1101. doi:10.1109/TMC.2011.131

    Article  Google Scholar 

  20. Radosavac S, Baras JS, Koutsopoulos I (2005) A framework for mac protocol misbehavior detection in wireless networks. In: Proceedings of the 4th ACM workshop on wireless security, WiSe ’05. ACM, New York, NY, USA, pp 33–42. doi:10.1145/1080793.1080801

  21. Radosavac S, Moustakides G, Baras JS, Koutsopoulos I (2008) An analytic framework for modeling and detecting access layer misbehavior in wireless networks. ACM Trans Inf Syst Secur 11(4):19:1–19:28. doi:10.1145/1380564.1380567

    Article  Google Scholar 

  22. Raya M, Aad I, Hubaux JP, El Fawal A (2006) Domino: Detecting mac layer greedy behavior in ieee 802.11 hotspots. IEEE Trans Mob Comput 5(12):1691–1705. doi:10.1109/TMC.2006.183

    Article  Google Scholar 

  23. Raya M, Hubaux JP, Aad I (2004) Domino: A system to detect greedy behavior in ieee 802.11 hotspots. In: Proceedings of the 2nd international conference on mobile systems, applications, and services, MobiSys ’04. ACM, New York, NY, pp. 84–97. doi:10.1145/990064.990077

  24. Rong Y, Lee SK, Choi HA (2006) Detecting stations cheating on backoff rules in 802.11 networks using sequential analysis. In: Proceedings of the 25th IEEE international conference on computer communications, INFOCOM 2006. pp 1–13. doi:10.1109/INFOCOM.2006.305

  25. Shi F, Baek J, Song J, Liu W (2013) A novel scheme to prevent mac layer misbehavior in ieee 802.11 ad hoc networks. Telecommun Syst 52(4):2397–2406. doi:10.1007/s11235-011-9552-y

    Article  Google Scholar 

  26. Szott S (2014) Selfish insider attacks in ieee 802.11s wireless mesh networks. IEEE Commun Mag 52(6):227–233. doi:10.1109/MCOM.2014.6829968

    Article  Google Scholar 

  27. Tang J, Cheng Y, Zhuang W (2011) An analytical approach to real-time misbehavior detection in ieee 802.11 based wireless networks. In: Proceedings of the IEEE INFOCOM, 2011. pp 1638–1646. doi:10.1109/INFCOM.2011.5934957

  28. Tang J, Cheng Y, Zhuang W (2014) Real-time misbehavior detection in ieee 802.11-based wireless networks: An analytical approach. IEEE Trans Mob Comput 13(1):146–158. doi:10.1109/TMC.2012.227

    Article  Google Scholar 

  29. Toledo A, Wang X (2007) Robust detection of selfish misbehavior in wireless networks. IEEE J Sel Areas Commun 25(6):1124–1134. doi:10.1109/JSAC.2007.070807

    Article  Google Scholar 

  30. Wang X, Vasilakos AV, Chen M, Liu Y, Kwon TT (2012) A survey of green mobile networks: Opportunities and challenges. Mob Netw Appl 17(1):4–20. doi:10.1007/s11036-011-0316-4

    Article  Google Scholar 

  31. Yu C M, Chen C Y, Chao H C (2015) Proof of ownership in deduplicated cloud storage with mobile device efficiency. Network, IEEE 29(2):51–55

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Thaier Hayajneh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hayajneh, T., Almashaqbeh, G. & Ullah, S. A Green Approach for Selfish Misbehavior Detection in 802.11-Based Wireless Networks. Mobile Netw Appl 20, 623–635 (2015). https://doi.org/10.1007/s11036-015-0605-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-015-0605-4

Keywords

Navigation