Advertisement

Cluster Computing

, Volume 21, Issue 1, pp 51–63 | Cite as

Multi-level trust based intelligence intrusion detection system to detect the malicious nodes using elliptic curve cryptography in MANET

  • Opinder SinghEmail author
  • Jatinder Singh
  • Ravinder Singh
Article
  • 185 Downloads

Abstract

Mobile ad hoc networks (MANETs) are qualified by multi-hop wireless links and resource restrained nodes. Generally, mobile ad hoc networks (MANETs) are susceptible to various attacks like gray hole attack, black hole attack, selective packet dropping attack, Sybil attack, and flooding attack. Therefore, the wireless network should be protected using encryption, firewalls, detection schemes to identify the attackers and decreasing their impact on the network. So, it’s an essential task to design the intelligent intrusion detection system. This research work deals with designing the multilevel trust based intelligence intrusion detection system with cryptography schemes for detecting the attackers. In order to identify the attackers, we propose a novel trust management with elliptic curve cryptography (ECC) algorithm. At first, a trust manager is maintained, its functions is to classify the trust into three different sets of trust level based upon the elliptic curve cryptography and Schnorr’s signature in the MANET. Each trust level has identified a single attacker. Thus, the proposed method has detected three types of attackers such as black hole attack, flooding attack and selective packet dropping attack. Furthermore, it have provided countermeasure for these attackers in the MANET as well as improved performances. Hence, it obtains higher throughput, minimum delay, minimum packet loss and efficient end to end delivery in MANET. Thus, the proposed scheme is a secure and optimal solution to encounter attackers, which represents to be efficient and significant.

Keywords

Multilevel trust based intelligent intrusion detection system Mobile ad hoc networks (MANETs) Trust management Elliptic curve cryptography (ECC) algorithm and malicious node 

Notes

Acknowledgements

Authors are highly thankful to the Department of RIC, IKG Punjab Technical University, Kapurthala, Punjab, India for providing the opportunity to conduct this research work.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

References

  1. 1.
    Siva Ram Murthy, C., Manoj, B.S.: Ad Hoc Wireless Networks, Architecture, and Protocols. Prentice Hall PTR, Upper Saddle River (2004)Google Scholar
  2. 2.
    Basagni, S., Conti, M., Giordano, S., Stojmenovic, I.: Mobile Ad Hoc Networks. IEEE Press, Wiley (2003)Google Scholar
  3. 3.
    Aggelou, G., et al.: Mobile Ad Hoc Networks, 2nd edn. McGraw Hill Professional Engineering, Oxford (2004)Google Scholar
  4. 4.
    Chlamtac, I., Conti, M., Liu, J.J.-N.: Mobile ad hoc networking: imperatives and challenges. Elsevier Netw. Mag. 13, 13–64 (2004)Google Scholar
  5. 5.
    Belding-Royer, E.M., Toh, C.K.: A review of current routing protocols for ad-hoc mobile wireless networks. IEEE Pers. Commun. Mag. 6(2), 46–55 (1999)CrossRefGoogle Scholar
  6. 6.
    Banerjee, S.: Detection/removal of cooperative black and gray hole attack in mobile ad-hoc networks. In: Proceedings of the World Congress on Engineering and Computer Science (2008)Google Scholar
  7. 7.
    Jain, S., Jain, M., Kandwal, H.: Advanced algorithm for detection and prevention of cooperative black and gray hole attacks in mobile ad hoc networks. J. Comput. Appl. 1(7), 37–42 (2010)Google Scholar
  8. 8.
    Agrawal, P., Ghosh, R.K., Das, S.K.: Cooperative black and gray hole attacks in mobile ad hoc networks. In: Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication, pp. 310–314. Suwon, Korea (2008)Google Scholar
  9. 9.
    Baadache, A., Belmehdi, A.: Avoiding black hole and cooperative black hole attacks in wireless ad hoc networks. J. Comput. Sci. Inf. Secur. 7(1), 10–16 (2010)Google Scholar
  10. 10.
    Bace, R., et al.: An Introduction to Intrusion Detection & Assessment. Infidel Inc., prepared for ICSA Inc. Copyright 1998 (1998)Google Scholar
  11. 11.
    Richard. M., et al.: Intrusion Detection FAQ: Are their limitations of Intrusion Signatures. http://www.sans.org/security-resources/idfaq/ limitations. PHP, April 5 (2001)
  12. 12.
    Kozushko, H.: Intrusion detection: host based and network-based intrusion detection systems. Indep. Study 11, 1–23 (2003)Google Scholar
  13. 13.
    Abraham, A., et al.: SCIDS: a soft computing intrusion detection system. In: WDC 2004. LNCS, School of Computer Science and Engineering, Chung-Ang University, Korea, Springer, Berlin, Heidelberg, vol. 3326, pp. 252–257 (2004)Google Scholar
  14. 14.
    Toosi, A.N., Kahani, M.: A new approach to intrusion detection based on an evolutionary soft computing model using neuro-fuzzy classifiers. Comput. Commun. 30(10), 2201–2212 (2005)CrossRefGoogle Scholar
  15. 15.
    Abraham, A., Jain, R., Thomas, J., Han, S.Y.: D-SCIDS: distributed soft computing intrusion detection system. J. Netw. Comput. Appl. 30(82), 81–98 (2007)CrossRefGoogle Scholar
  16. 16.
    Hubballi, N., et al.: Fuzzy mega cluster based anomaly network intrusion detection, In: International Conference on Network and Service Security, 2009. N2S ’09. ISBN: 978-2-9532-4431-1 (2009)Google Scholar
  17. 17.
    Wang, F., Chen, H., Zhao, J., Rong, C.: IDMTM: a novel intrusion detection mechanism based on trust model for ad hoc networks. In: 22nd International Conference on Advanced Information Networking and Applications, AINA 2008, pp. 978, 984Google Scholar
  18. 18.
    Gonzalez, O.F., Ansa, G., Howarth, M., Pavlou, G.: Detection and accusation of packet forwarding misbehavior in mobile ad-hoc networks. J. Internet Eng. 2(1), 181–192 (2008)Google Scholar
  19. 19.
    Nadeem, A., Howarth, M.: Adaptive intrusion detection & prevention of denial of service attacks in MANETs. In: ACM, 2009 (2009)Google Scholar
  20. 20.
    Wei, Z., et al.: Security enhancements for mobile ad hoc networks with trust management using uncertain reasoning. IEEE Trans. Veh. Technol. 63(9), 4647–4658 (2014)CrossRefGoogle Scholar
  21. 21.
    Biwas, S., et al.: Trust based energy efficient detection and avoidance of black hole attack to ensure secure routing in MANET. In: Applications and Innovations in Mobile Computing (AIMoC), 2014 (2014)Google Scholar
  22. 22.
    Agarwal, U., Yadav, K.P., Tiwari, U.: Security threats in mobile ad hoc networks. Int. J. Res. Sci. Technol. 2(2), 53–64 (2015)Google Scholar
  23. 23.
    Issariyakul, T., Hossain, E.: Introduction to Network Simulator NS2. Springer, New York (2009)CrossRefGoogle Scholar
  24. 24.
    Sajjad, S.M.: Neighbor node trust based intrusion detection system for WSN. In: 6th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN-2015 (2015)Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.IKG PTUKapurthalaIndia

Personalised recommendations