A Misbehavior-Based Reputation Management System for VANETs

Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 181)

Abstract

Detecting misbehavior in vehicular ad-hoc networks is very important problem with wide range of implications including safety related and congestion avoidance applications. Most misbehavior detection schemes are concerned with detection of malicious nodes. In most situations, vehicles would send wrong information because of selfish reasons of their owners. Because of rational behavior, it is more important to detect false information than to identify misbehaving nodes. In this paper, we propose a misbehavior-based reputation management system (MBRMS) which is composed of three components: misbehavior detection, event rebroadcast and global eviction algorithms, to detect and filter false data in vehicular ad-hoc networks (VANETs). The performance of MBRMS is evaluated through simulation. From the results of the simulation, we confirm that the proposed MBRMS identifies and evicts properly bad nodes by outlier detection method and misbehaving node risk value.

Keywords

Event rebroadcast eviction algorithm misbehavior detection outlier detection reputation system VANETs 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Al-Qutayri, M., et al.: Security and Privacy of Intelligent VANETs. In: InTech, Rijeka (2010)Google Scholar
  2. 2.
    Ding, Q., et al.: Reputation Management in Vehicular Ad Hoc Networks. In: International Conference on Multimedia Technology, pp. 1–5. IEEE Press, Ningbo (2010)Google Scholar
  3. 3.
    Zang, J.: A Survey of Trust Management for VANETs. In: IEEE International Conference on Advanced Information Networking and Applications, pp. 105–112. IEEE Press, Singapore (2011)CrossRefGoogle Scholar
  4. 4.
    Huang, Z.: On Reputation and Data-centric Misbehavior Detection Mechanisms for VANET. Master’s Thesis, School of Electrical and Eng. & Computer Science, University of Ottawa (2011)Google Scholar
  5. 5.
    Hong, X., et al.: SAT: Situation-Aware Trust Architecture for Vehicular Networks. In: Proceedings of the 3rd ACM International Workshop on Mobility in the Evolving Internet, pp. 1–8. ACM Press, Seattle (2007)Google Scholar
  6. 6.
    Ruj, S., et al.: On Data-Centric Misbehavior Detection in VANETs. In: Proceeding of Vehicular Technology Conference, pp. 1–5. IEEE Press, San Francisco (2011)Google Scholar
  7. 7.
    Zhuo, X., et al.: Removal of Misbehaving Insiders in Anonymous VANETs. In: Proceedings of the 12th ACM international conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pp. 106–115. ACM Press, Canary Islands (2009)Google Scholar
  8. 8.
    Sha, K., et al.: RD4: Role-Differentiated Cooperative Deceptive Data Detection and Filtering in VANETs. IEEE Transactions on Vehicular Technology 59(3), 1183–1190 (2010)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Mac Parthalain, N., Shen, Q.: On rough sets, their recent extensions and applications. The Knowledge Engineering Review 25(4), 365–395 (2010)CrossRefGoogle Scholar
  10. 10.
    Zhang, K., Hutter, M., Jin, H.: A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS, vol. 5476, pp. 813–822. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  11. 11.
    Pratap, R.: Getting Started with MATLAB. OXFORD university press, New York (2010)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  1. 1.School of IT EngineeringCatholic University of DaeguGyeongsanRepublic of Korea

Personalised recommendations