Skip to main content

Robust Network Intrusion Detection Scheme Using Long-Short Term Memory Based Convolutional Neural Networks

Abstract

The intrusion detection system (IDS) is a crucial part in the network administration system to detect some types of cyber attack. IDS is categorized as a classifying machine thus it is likely to engage with the machine learning schemes. Many studies have demonstrated how to apply machine learning schemes to IDS even though they cannot provide optimum results. To tackle this issue, deep learning schemes can be considered as the solution due to its achievement in several fields. Therefore, in this study, we propose a deep learning model which is constructed based on convolutional neural network (CNN) layers and using Long-Short Term Memory (LSTM) layers called CNN-LSTM to classify every single traffic network. We use NSL-KDD dataset as the benchmark thus we can compare the performance of our proposed method with other existing works. This dataset includes two testing sets which are the first one is KDDTest+ while the second one is KDDTest− 21 which is more difficult to be classified. The results show that our proposed method outperforms other existing works.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  1. 1.

    Sharma S, Gupta RK (2015) Intrusion detection system: a reivew. Int. J. Secur. App. 9 (5):69–76

    Google Scholar 

  2. 2.

    Revathi S, Malathi A (2013) A detailed analysis on NSL- KDD dataset using various machine learning techniques for intrusion detection. Int. J. Eng. Res. Technol 2:1848–1853

    Google Scholar 

  3. 3.

    Dhanabal L, Shantharajah SP (2015) A study on NSL-KDD dataset for intrusion detection system based on classification algorithms. International Journal of Advanced Research in Computer and Communication Engineering 4:446–452

    Google Scholar 

  4. 4.

    Kuang F, Xu W, Zhang S (2014) A novel hybrid KPCA and SVM with GA model for intrusion detection. Appl Soft Comput 18:178–184

    Article  Google Scholar 

  5. 5.

    Hettich S, Bay SD (1999) The UCI KDD Archive [http://kdd.ics.uci.edu]. Irvine, CA: University of California, Department of Information and Computer Science

  6. 6.

    Reddy RR, Ramadevi Y, Sunitha KVN (2016) “Effective discriminant function for intrusion detection using SVM”. In: International Conference on Advances in Computing Communications and Informatics (ICACCI), pp 1148–1153

  7. 7.

    Ibrahim LM, Basheer DT, Mahmod MS (2013) “A comparison study for intrusion database (KDD 99, NSL-KDD) based on self organization map (SOM) artificial neural network”. J Eng. Sci. Technol. 8:107–119

    Google Scholar 

  8. 8.

    Ingre B, Yadav A (2015) Performance analysis of NSL-KDD dataset using ANN

  9. 9.

    Farnaaz N, Jabbar MA (2016) Random forest modeling for network intrusion detection system. Procedia Computer Science 89:213–217

    Article  Google Scholar 

  10. 10.

    Zhang J, Zulkernine M, Haque A (2008) Random-Forests-Based Network intrusion detection systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 38:649–659

    Article  Google Scholar 

  11. 11.

    Niyaz Q, Sun W, Javaid AY, Alam M (2015) A deep learning approach for network intrusion detection system. EAI Endorsed Transactions on Security and Safety 16:21–26

    Google Scholar 

  12. 12.

    Xu Y, Shi L, Ni Y (2017) Deep-learning-based Scenario Generation Strategy Considering Correlation Between Multiple Wind Farms. The Journal of Engineering 2017:2207–2210

    Article  Google Scholar 

  13. 13.

    Wu B-F, Lin C-H (2018) Adaptive feature mapping for customizing deep learning based facial expression recognition model. IEEE Access 6:12451–12461

    Article  Google Scholar 

  14. 14.

    Wang T, Wen C-K., Wang H, Gao F, Jiang T, Jin S (2017) Deep learning for wireless physical layer: opportunities and challenges. China Communications 14:92–11

    Article  Google Scholar 

  15. 15.

    Yin C, Zhu Y, Fei J, He X (2017) A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access 5:21954–21961

    Article  Google Scholar 

  16. 16.

    Kaushik SS, Deshmukh PR (2011) Detection of attacks in an intrusion detection system. International Journal of Computer Science and Information Technologies 2(3):982–986

    Google Scholar 

  17. 17.

    Hochreiter S, Schmidhuber J (1997) Long-Short Term memory. Neural Comput 9:1735–1780

    Article  Google Scholar 

  18. 18.

    LeCun Y, Haffner P, Bottou L, Bengio Y (1999) Object recognition with Gradient-Based learning

  19. 19.

    Shen Y, Zheng K, Wu C, Zhang M, Niu X, Yang Y (2018) An Ensemble Method Based on Selection ba Algorithm for Intrusion Detection. The Computer Journal 61:526–538

    Article  Google Scholar 

  20. 20.

    Xu C, Shen J, Du X, Zhang F (2018) An intrusion detection system using a deep neural network with gated recurrent units. IEEE Access 6:48697–48707

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Jenq-Shiou Leu.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hsu, CM., Azhari, M.Z., Hsieh, HY. et al. Robust Network Intrusion Detection Scheme Using Long-Short Term Memory Based Convolutional Neural Networks. Mobile Netw Appl 26, 1137–1144 (2021). https://doi.org/10.1007/s11036-020-01623-2

Download citation

Keywords

  • Intrusion detection system
  • Deep learning
  • Long-short term memory
  • NSL-KDD dataset