Cluster Computing

, Volume 18, Issue 4, pp 1549–1559 | Cite as

Stochastic model: reCAPTCHA controller based co-variance matrix analysis on frequency distribution using trust evaluation and re-eval by Aumann agreement theorem against DDoS attack in MANET

  • M. Poongodi
  • S. Bose


The DDoS attack is more vulnerable attack in the self structured and infra structure less MANET environment. The bot net based flooding attack is very cheap and easy to implement. Many detection and prevention mechanisms has been proposed to avoid DDoS attack. Here we have proposed the stochastic model of covariance matrix analysis in which the entropy difference has been computed by the frequency distribution of rule metrics against the threshold entropy periodically which in turn difference is highest can be controlled by reCAPTCHA controller to defend against DDoS attack. The prevention mechanism by trust evaluation using clustering methodology used to identify the attacker and reduce the cost of detection mechanism. The Aumann agreement theorem based trust re-evaluation is also proposed in order to reduce the false positive and negative probabilities, such that the accuracy of the system is enhanced. Finally the nodes identified as attacker by their trust values will be evicted and rekeying has been done. The experimental analysis in NS2 with CAIDA dataset shows true positive rate and detection rate is high in the proposed mechanism.


reCAPTCHA controller DDoS Stochastic model Entropy Rule metrics 


  1. 1.
    Poongodi, M., Bose, S.: Design of intrusion detection and prevention system (IDPS) using DGSOTFC in collaborative protection networks. In: 2013 fifth international conference on advanced computing (ICoAC). IEEE, (2013)Google Scholar
  2. 2.
    Poongodi, M., Bose, S., Ganesh Kumar, N.: The effective intrusion detection system using optimal feature selection algorithm. Int. J. Enterp. Netw. Manag. (2015)
  3. 3.
    Poongodi, M., Bose, S.: The COLLID based intrusion detection system for detection against DDOS attacks using trust evaluation. Adv. Nat. Appl. Sci. 9(6), 574–580 (2015)Google Scholar
  4. 4.
    Yin, M., Feng, J., Tang, Y.: An overview on node behavior trust evaluation in ad hoc network. Advances in Wireless Sensor Networks. Springer, Berlin (2013)Google Scholar
  5. 5.
    Cho, J.-H., Chen, I.-R.: Modeling and analysis of intrusion detection integrated with batch rekeying for dynamic group communication systems in mobile ad hoc networks. Wirel. Netw. 16(4), 1157–1173 (2010)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Lang, R., Deng, Z.: Data distribution algorithm using time based weighted distributed hash tables. In: Proceedings of 7th international conference on grid and cooperative computing, pp. 210–213, 24–26 Oct 2008Google Scholar
  7. 7.
    Zhang, H., Goel, A., Govindan, R.: Improving lookup latency in distributed hash table systems using random sampling. IEEE/ACM Trans. Netw. 13(5), 1121–1134 (2005)CrossRefGoogle Scholar
  8. 8.
    Xia, P., Wu, M., Wang, K., Chen, X.: Identity-based fully distributed certificate authority in an OLSR MANET’. 4th international conference on wireless communications, networking and mobile computing, 2008. WiCOM ’08, pp. 1–4, 12–14 Oct 2008Google Scholar
  9. 9.
    Thostle, J.: Applying network address encryption to anonymity and preventing data exfiltration. Military communications conference, MILCOM 2008. IEEE 2008, pp. 1–7 (2008)Google Scholar
  10. 10.
    Eissa, T., Razak, S.A., Ngadi, M.A.: Enhancing MANET security using secret public keys. In: International conference on future networks, pp. 130–134 (2009)Google Scholar
  11. 11.
    Qin, Y., Huang, D.: A statistical traffic pattern discovery system for MANETs. IEEE Trans. Depend. Sec. Comput. 11(2), 181–192 (2014)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Biswas, S., Dey, P.: Secure check pointing-recovery using trusted nodes in MANET. In: 4th international conference on computer and communication technology 2013, pp. 175–180Google Scholar
  13. 13.
    François, J., Aib, I., Boutaba, R.: FireCol: a collaborative protection network for the detection of flooding DDoS attacks. IEEE/ACM Trans. Netw. (TON) 20(6), 1828–1841 (2012)Google Scholar
  14. 14.
    Luo, F., Khan, L., Bastani, F., Yen, I.L., Zhou, J.: A dynamically growing self-organizing tree (DGSOT) for hierarchical clustering gene expression profiles. Bioinformatics 20, 2605–2617 (2004)CrossRefGoogle Scholar
  15. 15.
    Perkins, D.D.: Factors affecting the performance of ad hoc networks. In: IEEE international conference on communications, vol. 4, pp. 2048–2052 (2002)Google Scholar
  16. 16.
    Wei, Z., Tang, H., Yu, F.R., Wang, M., Mason, P.: Security enhancements for mobile ad hoc networks with trust management using uncertain reasoning. IEEE Trans. Veh. Technol. 63, 4647–4658 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Anna UniversityChennaiIndia

Personalised recommendations