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Latency Aware Reliable Intrusion Detection System for Ensuring Network Security

  • L. SheebaEmail author
  • V. S. Meenakshi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 750)

Abstract

In network communication based applications, Intrusion detection system plays a vital role. In these applications, so as to collapse the system, the malevolent nodes may create enormous amount of traffic. In order to identify and prevent the intrusion activities, there are numerous researches that are presented by diverse researchers. LEoNIDS: architecture solves the energy-latency tradeoff by giving minimum power utilization as well as minimum detection latency all at once. However LEoNIDS architecture focus on the latency but not the precise detection of intrusion. This work resolves these issues by presenting the technique called Attack Feature based Fast and Accurate Intrusion Detection System (AF-FAIDS). This work presents a solution that enables intrusion detection in an effective way with enhanced delay and latency parameter. This also track and eliminate the intrusion attack existing in the system in an effective way with guaranteed energy saving. By using Machine learning, methods like Support Vector Machine, attack detection ratio is enhanced that would learn the attacks features in an effective way. Those features, which are taken in this presented technique, are Average Length of IP Flow, One-Way Connection Density, Incoming and Outgoing Ratio of IP Packets, Entropy of Protocols as well as Length in IP Flow. This technique provides the precise and quicker identification of intrusion attacks that will decrease the latency. The technical work is carried out in the NS2 environment under numerous performance measures and it is confirmed that the presented technique gives improved outcome when compared with the previous research methodologies.

Keywords

Intrusion detection system High throughput Low latency Attack feature Learning method 

Notes

Acknowledgements

I would like to show my warm thanks to Dr. V. S. Meenakshi , Research supervisor who’s guidance proved to be a milestone effort toward the success of my research. I also wish to pay my special acknowledgement with high sense of appreciation, for the love and support of my family and my parents Mr. K. Lakshmanan and Mrs. L. Kalaiselvi and also my heartfelt thanks to my husband D. Selvaraj without whom the research is not possible. I would also like to extend my thanks to my friends who assisted me throughout the completion of this research and made me achieve my goal. Finally, wishing to recognize the valuable help of all provided during my research.

References

  1. 1.
    Cárdenas, A.A., Berthier, R., Bobba, R.B., Huh, J.H., Jetcheva, J.G., Grochocki, D., Sanders, W.H.: A framework for evaluating intrusion detection architectures in advanced metering infrastructures. IEEE Trans. Smart Grid 5(2), 906–915 (2014)CrossRefGoogle Scholar
  2. 2.
    Depren, O., Topallar, M., Anarim, E., Ciliz, M.K.: An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks. Expert Syst. Appl. 29(4), 713–722 (2005)CrossRefGoogle Scholar
  3. 3.
    Vasudevan, A., Harshini, E., Selvakumar, S.: SSENet-2011: a network intrusion detection system dataset and its comparison with KDD CUP 99 dataset. In: IEEE International Conference on Internet (AH-ICI), Second Asian Himalayas, pp. 1–5 (2011)Google Scholar
  4. 4.
    Garcia-Teodoro, P., Diaz-Verdejo, J., Maciá-Fernández, G., Vázquez, E.: Anomaly-based network intrusion detection: techniques, systems and challenges. Comput. Secur. 28(1–2), 18–28 (2009)CrossRefGoogle Scholar
  5. 5.
    Duhan, S.: Intrusion Detection System in Wireless Sensor Networks: A Comprehensive Review, pp. 2707–2713 (2016)Google Scholar
  6. 6.
    Srinivasan, T., Vivek V., Chandrasekar, R.: A self-organized agent-based architecture for power-aware intrusion detection in wireless ad-hoc networks. In: IEEE International Conference on Computing & Informatics, pp. 1–6 (2006)Google Scholar
  7. 7.
    Bahrololum, M., Salahi, E., Khaleghi, M.: Machine learning techniques for feature reduction in intrusion detection systems: a comparison. In: IEEE Fourth International Conference on Computer Sciences and Convergence Information Technology, pp. 1091–1095 (2009)Google Scholar
  8. 8.
    Hu, X., Runzi B.: Research on intrusion detection model of wireless sensor network. In: IEEE International Conference on Computer Science and Service System (CSSS), pp. 3471—3474 (2011)Google Scholar
  9. 9.
    Iftikhar, A., Azween, B.A., Abdullah, S.A.: Artificial neural network approaches to intrusion detection: a review. Telecommunications and Informatics Book as ACM guide Included in ISI/SCI Web of Science and Web of Knowledge (2009)Google Scholar
  10. 10.
    Badgujar, T., More, P.: A review for an intrusion detection system combined with neural network. Int. J. 4(3) (2014)Google Scholar
  11. 11.
    Pradhan, M., Pradhan, S.K., Sahu, S.K.: Anomaly detection using artificial neural network. Int. J. Eng. Sci. Emerg. Technol. 2(1), 29–36 (2012)Google Scholar
  12. 12.
    Mukkamala, S., Janoski, G., Sung, A.: Intrusion detection using neural networks and support vector machines. IEEE Int. Joint Conf. Neural Netw. 2, 1702–1707 (2002)zbMATHGoogle Scholar
  13. 13.
    Somwanshi, P.D., Chaware, S.M.: A review on: advanced artificial neural networks (ANN) approach for IDS by layered method. Int. J. Comput. Sci. Inf. Technol. (IJCSIT) 5(4), 5129–5131 (2014)Google Scholar
  14. 14.
    Maia, J.E.B., Barreto, G.A., Coelho, A.L.: On self-organizing feature map (SOFM) formation by direct optimization through a genetic algorithm. In: IEEE Eighth International Conference on Hybrid Intelligent Systems, pp. 661–666 (2008)Google Scholar
  15. 15.
    Afrah, N.: A comparative study of different artificial neural networks based intrusion detection systems. Int. J. Sci. Res. Publ. 3(7) (2013)Google Scholar
  16. 16.
    Aizenberg, I., Claudio, M.: Multilayer feed forward neural network based on multi-valued neurons (MLMVN) and a back propagation learning algorithm. Soft. Comput. 11(2), 169–183 (2007)CrossRefGoogle Scholar
  17. 17.
    Nazir, A.: A comparative study of Cascaded forward back propagation and hybrid SOFM-CFBP neural networks based intrusion detection systems. Int. J. Sci. Eng. Res. 4(6), 2447–2452 (2013)Google Scholar
  18. 18.
    Ashfaq, R.A.R., Wang, X.Z., Huang, J.Z., Abbas, H., He, Y.L.: Fuzziness based semi-supervised learning approach for intrusion detection system. Inf. Sci. 378, 484–497 (2017)CrossRefGoogle Scholar
  19. 19.
    Khari, M., Karar, A.: Analysis on intrusion detection by machine learning techniques: a review. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(4) (2013)Google Scholar
  20. 20.
    Tsikoudis, N., Papadogiannakis, A., Markatos, E.P.: LEoNIDS: a low-latency and energy-efficient network-level intrusion detection system. IEEE Trans. Emerg. Topics Comput. 4(1), 142–155 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Bharathiyar UniversityCoimbatoreIndia
  2. 2.Chikkanna Government Arts CollegeTirupurIndia

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