This work is to detect and prevent unprecedented data identified from lightweight resource constraint mobile sensor devices. In this work, event or error detection technique of Traag et. al., local-global outlier algorithm of Branch et. al., Teo and Tan’s protocol of group key management and Cerpa et. al protocol of Frisbee construction are integrated and modified for lightweight resource constraint devices [20][22]-[24]. The proposed technique in this work is better than other techniques because of: (a) scalability, (b) optimization of resources, (c) energy efficient and (d) secure in terms of collision resistant, compression, backward and forward secrecy. The deviations in modified form of proposed mechanism are corrected using virtual programmable nodes and results show that proposed scheme work with zero probability of error and attack.


lightweight outlier anomalies security key management MANET 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Zhou, L., Haas, Z.J.: Securing Ad Hoc Networks. IEEE Network 13(6), 24–30 (1999)CrossRefGoogle Scholar
  2. 2.
    Heady, R., Luger, G., Maccabe, A., Servilla, M.: The architecture of a network level instrusion detection system, Computer Science Department, University of New Mexico. Tech. Rep. (1990)Google Scholar
  3. 3.
    Chen, J., Kher, S., Somani, A.: Distributed fault detection of wireless sensor networks. In: Proceedings of the 2006 Workshop on Dependability Issues in Wireless Ad Hoc Networks and Sensor Networks, pp. 65–72 (2006)Google Scholar
  4. 4.
    Luo, X., Dong, M., Huang, Y.: On distributed fault tolerant detection in wireless sensor networks. IEEE Transactions on computers 55(1), 58–70 (2006)CrossRefGoogle Scholar
  5. 5.
    Krishnamachari, B., Iyengar, S.: Distributed Bayesian algorithms for fault tolerant event region detection in wireless sensor networks. IEEE Transactions on Computers 53(3), 241–250 (2004)CrossRefGoogle Scholar
  6. 6.
    Martincic, F., Schwiebert, L.: Distributed event detection in sensor networks. In: Proceedings of Systems and Network Communication, pp. 43–48 (2006)Google Scholar
  7. 7.
    Ding, M., Chen, D., Xing, K., Cheng, X.: Localized fault tolerant event boundary detection in sensor networks. In: Proceesings of IEEE Conference of Computer and Communications Socities, pp. 902–913 (March 2005)Google Scholar
  8. 8.
    Silva, A.P.R., Martins, M.H.T., Rocha, B.P.S., Loureiro, A.A.F.: Decentralized intrusion detection in wireless sensor networks. In: Proceedings of the 1st ACM international Workshop on Quality of Service and Security in Wireless and Mobile Networks, pp. 16–23 (2005)Google Scholar
  9. 9.
    Bhuse, V., Gupta, A.: Anomaly intrusion detection in wireless sensor networks. Journal of High Speed Networks 15(1), 33–51 (2006)Google Scholar
  10. 10.
    Jurdak, R., Wang, X.R., Obst, O., Valencia, P.: Wireless Sensor Network Anomalies: Diagnosis and Detection Strategies. In: Tolk, A., Jain, L.C. (eds.) Intelligence-Based Systems Engineering. ISRL, vol. 10, pp. 309–325. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Buxton, H.: Learning and understanding dynamic scene activity: A review. Image and Vision Computing 21, 125–136 (2003)CrossRefGoogle Scholar
  12. 12.
    Hu, W., Tan, T., Wang, L., Maybank, S.: A survey on visual surveillance of object motion and behaviors. IEEE Trans. Syst. Man Cybern., Appl. Rev. 34(3), 334–352 (2004)CrossRefGoogle Scholar
  13. 13.
    Chandola, V., Banerjee, A., Kumar, V.: Outlier Detection: A Survey. ACM Computing Surveys, 1–72 (2009)Google Scholar
  14. 14.
    Zhang, Y., Meratnia, N., Havinga, P.: Outlier Detection Techniques for Wireless Sensor Networks: A Survey. IEEE Communication Surveys & Tutorials 12(2) (2010)Google Scholar
  15. 15.
    Gogoi, P., Borah, B., Bhattacharyya, D.K.: Anomaly Detection Analysis of Intrusion Data using Supervised and Unsupervised Approach. Journal of Convergence Information Technology 5(1) (February 2010)Google Scholar
  16. 16.
    Gogoi, P., Bhattacharyya, D.K., Borah, B., Kalita, J.K.: A Survey of Outlier Detection Methods in Network Anomaly Identification. The Computer Journal 54(4), 570–588 (2011)CrossRefGoogle Scholar
  17. 17.
    Hawkins, D.M.: Ident fication of outliers. Chapman and Hall, London (1980)CrossRefGoogle Scholar
  18. 18.
    Knorr, E.M., Ng, R.T.: Algorithm for mining distance based outliers in large datasets. In: Proceedings of the 24th International Conference on Very Large Databases, New York, USA, pp. 392–403. Morgan Kaufmann (1998)Google Scholar
  19. 19.
    Karl, H., Williz, A.: Protocols and Architectures for Wireless Sensor Networks. John Wiley & Sons (2007)Google Scholar
  20. 20.
    Branch, J.W., Giannelia, C., Szymanski, B., Wolff, R., Kargupta, H.: In-Network Outlier Detection in Wireless Sensor Networks. Knowledge and Information Systems 31 (2012)Google Scholar
  21. 21.
    Shamir, A.: How to share a secret. Communications of the ACM 22(11), 612–613 (1979)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Teo, J.C.M., Tan, C.H.: Energy-Efficient and Scalable Group Key Agreement for Large Ad Hoc Networks. In: PE-WASUN’s 2005, October 10-13, pp. 114–121 (2005)Google Scholar
  23. 23.
    Kumar, A., Aggarwal, A.: Efficient Hierarchical Threshold Symmetric Group Key Management Protocol for Mobile Ad Hoc Networks. In: IC3, pp. 335–346 (2012)Google Scholar
  24. 24.
    Cerpa, A., Elson, J., Estrin, D., Girod, L., Hamilton, M., Zhao, J.: Habitat Monitoring: Application Driver for Wireless Communication Technology. In: Proceedings of the ACM SIGCOMM Workshop on Data Communication in Latin America and the Caribbean, San Jose, Costa Rica (2001)Google Scholar
  25. 25.
    Traag, V.A., Browet, A., Calabrese, F., Morlot, F.: Social Event Detection in Massive Mobile Phone Data Using Probabilistic Location Inference. In: Traag, V.A., Browet, A., Calabrese, F., Morlot, F. (eds.) SocialCom/PASSAT, October 9-11, pp. 625–628 (2011)Google Scholar
  26. 26.
    NS3 Simulator,
  27. 27.
  28. 28.
    ProVerif protocol verifier toolkit,
  29. 29.
    Burmester, M., Desmedt, Y.G.: A secure and efficient conference key distribution system. In: De Santis, A. (ed.) EUROCRYPT 1994. LNCS, vol. 950, pp. 275–286. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  30. 30.
    Yang, J., Wang, Y.: A New Outlier Detection Algorithms based on Markov chain. Advanced Materials Research 366, 456–459 (2012)CrossRefGoogle Scholar
  31. 31.
    Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: Identifying Density Based Local Outliers. In: Proceedings of the ACM SIGMOD Conference, Dallas, TX (May 2000)Google Scholar
  32. 32.
    Wang, B., Perrizo, W.: RDF: a density-based outlier detection method using vertical data representation. In: IEEE Int. Conference on Data Mining, pp. 503–506 (2004)Google Scholar
  33. 33.
    Rajagopalan, S., Karwoski, R., Bartholmai, B., Robb, R.: Quantitative image analytics for strtified pulmonary medicine. In: IEEE Int. Symposium on Biomedical Imaging (ISBI), pp. 1779–1782 (2012)Google Scholar
  34. 34.
    Cheminod, M., Bertolotti, I.C., Durante, L., Sisto, R., Valenzano, A.: Tools for cryptograhic protocols analysis: A technical and experimental comparison. Journal on Computer Standards & Interfaces 31(5), 954–961 (2009)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2013

Authors and Affiliations

  • Adarsh Kumar
    • 1
    • 2
  • Krishna Gopal
    • 2
  • Alok Aggarwal
    • 1
    • 3
  1. 1.Computer Science Engineering and Information Technology DepartmentJaypee Institute of Information TechnologyNoidaIndia
  2. 2.Jaypee Institute of Information TechnologyNoidaIndia
  3. 3.JP Institute of Engineering and TechnologyMeerutIndia

Personalised recommendations