Intrusion detection system using an optimized kernel extreme learning machine and efficient features


In the study of Intrusion Detection System (IDS) choosing proper combination of features is of great importance. Many researchers seek to obtain appropriate features with optimization algorithms. There are several optimization algorithms that can properly select a near-optimal combination of features to reach an improved IDS. Genetic Algorithms (GA) as one of the most powerful methods have been used in this research for feature selection. In this paper, voted outputs of built models on the GA suggested features of a more recent version of KDD CUP 99 dataset, NSL KDD, based on five different labels, have been gathered as a new dataset. Kernel Extreme Learning Machine (KELM), whose parameters have been optimally set by GA, is executed on the obtained dataset and results are collected. Based on IDS criteria, our proposed method can easily outperform general classification algorithms which use all the features of the employed dataset, especially in R2L and U2R with the accuracy of 98.73% and 98.22% respectively which is the highest among the current literature.

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5



Intrusion detection system


Genetic algorithms


Kernel extreme learning machine


Decision TREE


K-nearest neighbor


Multilayer perceptron


Support vector machine


Kernel principle component analysis


Cuttlefish algorithm


Growing hierarchical self-organizing maps


Fisher discriminant ratio


Online sequential extreme learning machine


Time-varying chaos particle swarm optimization


Multiple criteria linear programming






  1. 1

    Inayat Z, Gani A, Anuar N B, Khan M K and Anwar S 2016 Intrusion response systems: Foundations, design, and challenges. J. Netw. Comput. Appl. 62: 53–74

    Article  Google Scholar 

  2. 2

    Elhag S, Fernández A, Bawakid A, Alshomrani S and Herrera F 2015 On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on intrusion detection systems. Expert Syst. Appl. 42: 193–202

    Article  Google Scholar 

  3. 3 Accessed 10.28.99

  4. 4 2015

  5. 5

    Tavallaee M, Bagheri E, Lu W and Ghorbani A 2009 A detailed analysis of the KDD CUP 99 data set. In: Proceeding of the IEEE Symposium on Computational Intelligence for Security and Defense Applications, pp. 53–58

  6. 6

    de la Hoz E et al 2013 Network anomaly classification by support vector classifiers ensemble and non-linear projection techniques. Hybrid Artif. Intell. Syst. 103–111

  7. 7

    Singh R, Kumar H and Singla R 2015 An intrusion detection system using network traffic profiling and online sequential extreme learning machine. Expert Syst. Appl. 42: 8609–8624

    Article  Google Scholar 

  8. 8

    Lakhina S, Joseph S and Verma B 2010 Feature reduction using principal component analysis for effective anomaly–based intrusion detection on NSL-KDD. Int. J. Eng. Sci. Technol. 2: 1790–1799

    Google Scholar 

  9. 9

    Jain A K, Duin R P W and Jianchang M 2000 Statistical pattern recognition: a review. Pattern Anal. Mach. Intell. 22: 4–37

    Article  Google Scholar 

  10. 10

    Trunk G V 1979 A problem of dimensionality: a simple example. Pattern Anal. Mach. Intell. PAMI 1: 306–307

  11. 11

    Goldberg D E et al 1989 Genetic Algorithms in Search Optimization and Machine Learning, vol. 412 pp. 211

  12. 12

    Li W 2004 Using genetic algorithm for network intrusion detection. In: Proceedings of the United States Department of Energy Cyber Security Group, pp. 1–8

  13. 13

    Fidelis M V, Lopes H and Freitas A 2000 Discovering comprehensible classification rules with a genetic algorithm. In: Proceedings of the 2000 Congress on Evolutionary Computation 1, pp. 805–810

  14. 14

    Rastegari S, Hingston P and Lam C-P 2015 Evolving statistical rule sets for network intrusion detection. Appl. Soft Comput. 33: 348–359

    Article  Google Scholar 

  15. 15

    Eesa A S, Orman Z and Brifcani A M A 2015 Novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems. Expert Syst. Appl. 42: 2670–2679

    Article  Google Scholar 

  16. 16

    Sindhu S S S, Geetha S and Kannan A 2012 Decision tree based light weight intrusion detection using a wrapper approach. Expert Syst. Appl. 39: 129–141

    Article  Google Scholar 

  17. 17

    Kim G, Lee S B and Kim S 2014 A novel hybrid intrusion detection method integrating anomaly detection with misuse detection. Expert Syst. Appl. 41: 1690–1700

    Article  Google Scholar 

  18. 18

    Saied A, Overill R E and Radzik T 2016 Detection of known and unknown DDoS attacks using artificial neural networks. Neurocomputing 172: 385–393

    Article  Google Scholar 

  19. 19

    Wang G, Hao J, Mab J and Huang L 2010 A new approach to intrusion detection using artificial neural networks and fuzzy clustering. Expert Syst. Appl. 37: 6225–6232

    Article  Google Scholar 

  20. 20

    Meng W, Li W and Kwok L-F 2014 EFM: enhancing the performance of signature-based network intrusion detection systems using enhanced filter mechanism. Comput. Secur. 43: 189–204

    Article  Google Scholar 

  21. 21

    Lin W-C, Ke S-W and Tsai C-F 2015 CANN: an intrusion detection system based on combining cluster centers and nearest neighbors. Knowl. Based Syst. 78: 13–21

    Article  Google Scholar 

  22. 22

    Erfani S M, Rajasegarar S, Karunasekera S and Leckie C 2016 High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning. Pattern Recognit. 58: 121–134

    Article  Google Scholar 

  23. 23

    Catania C A, Bromberg F and Garino CG 2012 An autonomous labeling approach to support vector machines algorithms for network traffic anomaly detection. Expert Syst. Appl. 39: 1822–1829

    Article  Google Scholar 

  24. 24

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

    Article  Google Scholar 

  25. 25

    Tsang C-H, Kwong S and Wang H 2007 Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection. Pattern Recognit. 40: 2373–2391

    Article  Google Scholar 

  26. 26

    Shafi K and Abbass HA 2009 An adaptive genetic-based signature learning system for intrusion detection. Expert Syst. Appl. 36: 12036–12043

    Article  Google Scholar 

  27. 27

    de la Hoz E, de la Hoz E, Ortiz A, Ortega J and Martínez-Álvarez A 2014 Feature selection by multi-objective optimization: application to network anomaly detection by hierarchical self-organizing maps. Knowl. Based Syst. 71: 322–338

    Article  Google Scholar 

  28. 28

    De la Hoz E, De La Hoz E, Ortiz A, Ortega J and Prieto B 2015 PCA filtering and probabilistic SOM for network intrusion detection. Neurocomputing 164: 71–78

    Article  Google Scholar 

  29. 29

    Monowar, Bhuyan H, Bhattacharyya D K and Kalita J K 2016 A multi-step outlier-based anomaly detection approach to network-wide traffic. Inf. Sci. 348: 243–271

    Article  Google Scholar 

  30. 30

    Bamakan S M H, Wang H, Yingjie T and Shi Y 2016 An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization. Neurocomputing 199: 90–102

    Article  Google Scholar 

  31. 31

    Fossaceca J M , Mazzuchi T A and Sarkani S 2015 MARK-ELM: application of a novel multiple kernel learning framework for improving the robustness of network intrusion detection. Expert Syst. Appl. 42: 4062–4080

    Article  Google Scholar 

  32. 32

    Reeves C R and Rowe J E 2003 Genetic algorithms: principles and perspectives: a guide to GA theory. US: Springer, vol. 20, pp 112

    MATH  Google Scholar 

  33. 33

    Avci E and Coteli R 2012 A new automatic target recognition system based on wavelet extreme learning machine. Expert Syst. Appl. 39: 12340–12348

    Article  Google Scholar 

  34. 34

    Cheng C, Tay W P and Huang G B 2012 Extreme learning machines for intrusion detection. In: The 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1–8

  35. 35

    Creech G and Jiang F 2012 The application of extreme learning machines to the network intrusion detection problem. In: Numerical Analysis and Applied Mathematics ICNAAM: International Conference of Numerical Analysis and Applied Mathematics 1479, pp. 1506–1511

    Google Scholar 

  36. 36

    de Farias G P M, de Oliveira A L and Cabral G G 2012 Extreme learning machines for intrusion detection systems. Neural Inf. Process. 535–543

  37. 37

    Amiri F, Rezaei Yousefi M, Lucas C, Shakery A and Yazdani N 2011 Mutual information-based feature selection for intrusion detection systems. J. Netw. Comput. Appl. 34: 1184–1199

    Article  Google Scholar 

  38. 38

    Sangkatsanee P, Wattanapongsakorn N and Charnsripinyo C 2011 Practical real-time intrusion detection using machine learning approaches. Comput. Commun. 34: 2227–2235

    Article  Google Scholar 

  39. 39

    Pereira C R, Nakamura R Y M, Costa K A P and Papa J P 2012 An optimum-path forest framework for intrusion detection in computer networks. Eng. Appl. Artif. Intell. 25: 1226–1234

    Article  Google Scholar 

  40. 40

    Liu Y, Liu S and Zhao X 2017 Intrusion detection algorithm based on convolutional neural network. In: State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, Henan, China 2017 4th International Conference on Engineering Technology and Application ISBN: 978-1-60595-527-8

  41. 41

    Shone N, Ngoc T N , Phai V D and Shi Q 2017 A deep learning approach to network intrusion detection. IEEE Trans. Emerg. Top. Comput. Intell. 2: 1–16

    Google Scholar 

  42. 42

    Xin Y, Kong L, Liu Z, Chen Y, Li Y, Zhu H, Gao M, Hou H and Wang C 2017 Machine learning and deep learning methods for cybersecurity. IEEE Transl. Content Min. 1: 1–10

    Google Scholar 

  43. 43 Accessed 2013

Download references


The authors would like to express their gratitude toward reference [43] for putting NSL-KDD dataset in public access.

Author information



Corresponding author

Correspondence to Jamal Ghasemi.

Appendix A: Proposed feature by GA

Appendix A: Proposed feature by GA

Table 7

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ghasemi, J., Esmaily, J. & Moradinezhad, R. Intrusion detection system using an optimized kernel extreme learning machine and efficient features. Sādhanā 45, 2 (2020).

Download citation


  • Intrusion detection system (IDS)
  • genetic algorithms (GA)
  • feature selection
  • kernel extreme learning machine (KELM)