Skip to main content
Log in

Cross-layer based multiclass intrusion detection system for secure multicast communication of MANET in military networks

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Multicast communication of mobile ad hoc networks (MANET), rather than multiple unicast communication, delivers common content to more than one receiver at a time. Due to cutting-edge communication technology and advancements in terms of radio-mounted devices, groups in front-end war field, as well as rescue troops, are well connected to carry out their missions using multicast communication. The key to the success of military networks in a hostile environment is security and collaboration. Internal attacks are major threats to impose a great failure in their mission goal. We introduce a novel indirect internal stealthy attack and known direct internal stealthy attacks such as black hole and deny-to-forward attacks on tree-based multicast routing protocol. These internal attacks can induce the performance degradation in the multicast group. We design a distributed cross-layer based machine learning anomaly detection system for multicast communication of MANET. Using efficient multilayer features, rather than routing layer features alone, improve the accuracy of the Intrusion Detection System (IDS) in terms of detection of direct and indirect internal stealthy attacks. We evaluate the sensitivity, specificity and detection accuracy of well-known multiclass classifiers in combination with various feature subset selection algorithms. Since our problem with classification is a multiclass, the performance metrics calculated here are different from the binary classifiers. Our IDS is efficient, with respect to high true positives, very low false positives and less resource consumption even in the very challenging conditions of multicast communication of ad hoc networks.

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

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Buttyan, L., & Hubaux, J.-P. (2007). Security and cooperation in wireless networks: Thwarting malicious and selfish behavior in the age of ubiquitous computing. New York: Cambridge University Press.

    Book  Google Scholar 

  2. Yang, H., et al. (2014). Provably secure three-party authenticated key agreement protocol using smart cards. Computer Networks, 58, 29–38.

    Article  Google Scholar 

  3. Zhou, J., et al. (2015). 4S: A secure and privacy-preserving key management scheme for cloud-assisted wireless body area network in m-healthcare social networks. Information Sciences, 314, 255–276.

    Article  Google Scholar 

  4. The future of military communication on the battlefield. Defense systems: Knowledge technologies and net-enabled warfare. http://www.defensesystems.com/articles/2012/02/08/cover-story-military-communications-technologies.aspx.

  5. Mohapatra, P., Chao, G., & Jian, L. (2004). Group communications in mobile ad hoc networks. Computer, 37(2), 52–59.

    Article  Google Scholar 

  6. Obraczka, K., & Tsuduk, G. (1998). Multicast routing issues in ad hoc networks. IEEE International Conference on ICUPC ’98, 1, 751–756.

    Google Scholar 

  7. Khalil, I., & Bagchi, S. (2011). Stealthy attacks in wireless ad hoc networks: Detection and countermeasure. IEEE Transactions on Mobile Computing, 10(8), 1096–1112.

    Article  Google Scholar 

  8. He, D., et al. (2012). ReTrust: Attack-resistant and lightweight trust management for medical sensor networks. IEEE Transactions on Information Technology in Biomedicine, 16(4), 623–632.

    Article  Google Scholar 

  9. Yan, Z. et al. (2015). A security and trust framework for virtualized networks and software-defined networking. Security and Communication Networks. doi:10.1002/sec.1243.

  10. Yao, G., et al. (2015). Passive IP traceback: Disclosing the locations of IP spoofers from path backscatter. IEEE Transactions on Information Forensics and Security, 10(3), 471–484.

    Article  Google Scholar 

  11. Liu, B., et al. (2014). Toward incentivizing anti-spoofing deployment. IEEE Transactions on Information Forensics and Security, 9(3), 436–450.

    Article  Google Scholar 

  12. Jing, Q., et al. (2014). Security of the internet of things: Perspectives and challenges. Wireless Networks, 20(8), 2481–2501.

    Article  Google Scholar 

  13. Zheng, Y., et al. (2014). A survey on trust management for internet of things. Journal of Network and Computer Applications, 42, 120–134.

    Article  Google Scholar 

  14. Royer, E., & Perkins, C. (2000). Multicast ad-hoc on-demand distance vector (MAODV) routing. Internet Draft.

  15. Nguyen, H. L., & Nguyen, U. T. (2008). A study of different types of attacks on multicast in mobile ad hoc networks. Ad Hoc Networks, 6(1), 32–46.

    Article  Google Scholar 

  16. Curtmola, R., & Nita-Rotaru, C. (2009). BSMR: Byzantine-resilient secure multicast routing in multihop wireless networks. IEEE Transactions on Mobile Computing, 8(4), 445–459.

    Article  Google Scholar 

  17. Mo’men, A. M. A., Hamza, H. S., & Saroit, I. A. (2010). A survey on security enhanced multicast routing protocols in Mobile Ad hoc Networks. In High-capacity optical networks and enabling technologies (HONET ’10), pp. 262–268.

  18. Feng, H., Kuan, H., & Hao, M. (2010). S-MAODV: A trust key computing based secure Multicast Ad-hoc On Demand Vector routing protocol. In 3rd IEEE international conference on computer science and information technology (ICCSIT), Vol. 6, pp. 434–438.

  19. Moamen, A. A., Haitham, H. S., & Saroit, I. A. (2014). Secure multicast routing protocols in mobile ad-hoc networks. International Journal of Communication Systems, 27(11), 2808–2831.

    Google Scholar 

  20. Jing, D., Curtmola, R., & Nita-Rotaru, C. (2011). Secure high-throughput multicast routing in wireless mesh networks. IEEE Transactions on Mobile Computing, 10(5), 653–668.

    Article  Google Scholar 

  21. Mo’men, A. M. A., Hamza, H. S., & Saroit, I. A. (2010). New attacks and efficient countermeasures for multicast AODV. In High-capacity optical networks and enabling technologies (HONET ’10), pp. 51–57.

  22. Roy, S., Addada, V. G., Setia, S., & Jajodia, S. (2005). Securing MAODV: Attacks and countermeasures. In 2nd Annual IEEE communications society conference on sensor and ad hoc communications and networks, IEEE SECON’05, pp. 521–532.

  23. Pushpa, A. M., & Kathiravan, K. (2013). Secure multicast routing protocol against internal attacks in mobile ad hoc networks. In 7th IEEE GCC conference and exhibition (GCC’13), pp. 245–250, 17–20.

  24. Menaka, P. A., Kathiravan, K. (2013). Resilient PUMA (Protocol for Unified Multicasting through Announcement) against internal attacks in Mobile Ad hoc Networks. In IEEE intertional conference on advances in computing, communications & informatics (ICACCI’13), pp. 1906–1912.

  25. Shim, Y. (2006). A Secure multicast routing protocol for ad hoc networks with misbehaving nodes (pp. 591–600). Berlin: Springer-Verlag ICCSA.

  26. Peng, L., Song, G., Shui, Y., & Vasilakos, A. V. (2012). CodePipe: An opportunistic feeding and routing protocol for reliable multicast with pipelined network coding, INFOCOM, pp. 100–108.

  27. Peng, L., Song, G., Shui, Y., & Vasilakos, A. V. (2014). Reliable multicast with pipelined network coding using opportunistic feeding and routing. IEEE Transactions on Parallel & Distributed Systems, 25(12), 3264–3273.

    Article  Google Scholar 

  28. Zubair, M., Fadlullah, Z. M., Taleb, T., Vasilakos, A. V., Guizani, M., & Kato, N. (2010). DTRAB: Combating against attacks on encrypted protocols through traffic-feature analysis. IEEE/ACM Transactions Network, 18(4), 1234–1247.

    Article  Google Scholar 

  29. Nikos, K., & Christos, D. (2009). LIDF: Layered intrusion detection framework for ad-hoc networks. Ad Hoc Networks, 7(1), 171–182.

    Article  Google Scholar 

  30. Sergio, P., Mitrokotsa, A., Agustin, O., & Peris-Lopez, P. (2012). Evaluation of classification algorithms for intrusion detection in MANETs. Knowledge-Based Systems, 36, 217–225.

    Article  Google Scholar 

  31. Sevil, S., & Clark, J. A. (2011). Evolutionary computation techniques for intrusion detection in mobile ad hoc networks. Computer Networks, 55(15, 27), 3441–3457.

    Google Scholar 

  32. Aikaterini, M., & Christos, D. (2013). Intrusion detection in MANET using classification algorithms: The effects of cost and model selection. Ad Hoc Networks, 11(1), 226–237.

    Article  Google Scholar 

  33. Joseph, J. F. C., Bu-Sung, L., Das, A., & Boon-Chong, S. (2011). Cross-layer detection of sinking behavior in wireless ad hoc networks using SVM and FDA. IEEE Transactions on Dependable and Secure Computing, 8(2), 233–245.

    Article  Google Scholar 

  34. Wang, X., Lin, T., & Wong, J. (2005). Feature selection in intrusion detection system over mobile ad-hoc network. Technical Report: Computer Science, lowa State University.

  35. Larry, B. (2004). Applications of learning classifier systems. Studies in fuzziness and soft computing, Vol. 150, Springer, Berlin. ISBN 978-3-540-39925-4.

  36. Nguyen, H. T. (2012). Reliable machine learning algorithms for intrusion detection systems: Machine learning for information security and digital forensics, PhD thesis. Gjovik University College.

  37. Butun et al. (2014). A survey of intrusion detection systems in wireless sensor networks. IEEE Communications Surveys & Tutorials, 16(1), 266–282.

  38. Garca-Teodoro, P., Daz-Verdejo, J., Maci-Fernndez, G., & Vzquez, E. (2009). Anomaly-based network intrusion detection: Techniques, systems and challenges. Computers & Security, 28(12), 18–28.

    Article  Google Scholar 

  39. Anantvalee, T. (2006). A survey on intrusion detection in mobile ad hoc networks. In Y. Xiao, X. Shen, D. -Z. Du (Eds.), Wireless/mobile network security, pp. 170–196.

  40. Bhuyan, M. H., Bhattacharyya, D. K., & Kalita, J. K. (2014). Network anomaly detection: Methods, systems and tools. IEEE Communications Surveys & Tutorials, 16(1), 303–336.

    Article  Google Scholar 

  41. Nadeem, A., & Howarth, M. P. (2013). A survey of MANET intrusion detection & prevention approaches for network layer attacks. IEEE Communications Surveys & Tutorials, 15(4), 2027–2045.

    Article  Google Scholar 

  42. Zhang, Y., & Wenke, L. (2000). Intrusion detection in wireless ad hoc networks. In 6th annual international conference on mobile computing and networking, MobiCom’ 2000, Boston.

  43. Zhang, Y., Lee, W., & Huang, Y.-A. (2003). Intrusion detection techniques for mobile wireless networks. Wireless Networks, 9(5), 545–556.

    Article  Google Scholar 

  44. Zhu, Y., & Kunz, T. (2008). MAODV Implementation for NS-2.26, Technical Report SCE-04-01. Carleton University

  45. Hongmei, D., Li, W., & Agrawal, D. P. (2002). Routing security in wireless ad hoc networks. IEEE Communications Magazine, 40(10), 70–75.

    Article  Google Scholar 

  46. The network simulator, ns2. http://www.isi.edu/nsnam/ns/.

  47. Majid, K., Behzad, M., Vasile, P., Hamid, N., & Caro, L. (2010). Using classifier fusion techniques for protein secondary structure prediction. International Journal of Computational Intelligence in Bioinformatics and Systems Biology, 1(4), 418–434.

    Article  Google Scholar 

  48. Fernandez Caballero, J. C., Martine, F. J., Hervas, C., & Gutierrez, P. A. (2010). Sensitivity Versus accuracy in multiclass problems using memetic pareto evolutionary neural networks. IEEE Transactions on Neural Networks, 21(5), 750–770.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Menaka Pushpa Arthur.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Arthur, M.P., Kannan, K. Cross-layer based multiclass intrusion detection system for secure multicast communication of MANET in military networks. Wireless Netw 22, 1035–1059 (2016). https://doi.org/10.1007/s11276-015-1065-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-015-1065-2

Keywords

Navigation