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ANN Based Intrusion Detection Model

  • Seunghyun Park
  • Hyunhee ParkEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 927)

Abstract

Anomaly based Intrusion Detection Systems (IDSs) are known to achieve high accuracy and detection rate. However, a significant computational overhead is incurred in training and deploying them. In this paper, we aim to address this issue by proposing a simple Artificial Neural Network (ANN) based IDS model. The ANN based IDS model uses the feed forward and the back propagation algorithms along with various other optimization techniques to minimize the overall computational overhead, while at the same time maintain a high performance level. Experimental results on the benchmark CICIDS2017 dataset shows that the performance (i.e., detection accuracy) of the ANN based IDS model. Owing to its high performance and low computational overhead, the ANN with Adam optimizer based IDS model is a suitable candidate for real time deployment and intrusion detection analysis.

Notes

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2017R1C1B5017556).

References

  1. 1.
    Roesch, M.: Snort - lightweight intrusion detection for networks. In: Proceedings 13th USENIX Conference on System Administration, pp. 229–238 (1999)Google Scholar
  2. 2.
    Mukkamala, S., Janoski, G., Sung, A.: Intrusion detection using neural networks and support vector machines. In: Proceedings International Joint Conference on Neural Networks, pp. 1702–1707 (2002)Google Scholar
  3. 3.
    Yu, Y., Wu, H.: Anomaly intrusion detection based upon data mining techniques and fuzzy logic. In: Proceedings IEEE International Conference on Systems, Man, and Cybernetics, pp. 514–517 (2012)Google Scholar
  4. 4.
    CICFlowMeter for network traffic generator and analyser. Canadian institute for cybersecurity (CIC) (2017). https://www.unb.ca/cic/research/applications.html
  5. 5.
    Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.: A detailed analysis of the KDD CUP 99 data set. In: Proceedings IEEE Symposium on Computational Intelligence for Security and Defense Applications, pp. 1–6 (2009)Google Scholar
  6. 6.
    Sharafaldin, I., Lashkari, A., Ghorbani, A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: Proceedings 4th International Conference on Information Systems Security and Privacy, pp. 108–116 (2018)Google Scholar
  7. 7.
    Shiravi, A., Shiravi, H., Tavallaee, M., Ghorbani, A.: Toward developing a systematic approach to generate benchmark datasets for intrusion detection. Comput. Secur. 31(3), 357–374 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Graduate School of Information SecurityKorea UniversitySeoulSouth Korea
  2. 2.Department of Computer SoftwareKorean Bible UniversitySeoulSouth Korea

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