Analysis on Improving the Performance of Machine Learning Models Using Feature Selection Technique

  • N. Maajid Khan
  • Nalina Madhav C
  • Anjali Negi
  • I. Sumaiya ThaseenEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)


Many organizations deploying computer networks are susceptible to different kinds of attacks in the current era. These attacks compromise the confidentiality, integrity and availability of network systems. It is a big challenge to build a reliable network as several new attacks are being introduced by the attackers. The aim of this paper is to improve the performance of the various machine learning algorithms such as KNN, Decision Tree, Random Forest, Bagging Meta Estimator and XGBoost by utilizing feature importance technique. These classifiers are chosen as they perform superior to other base and ensemble machine learning techniques after feature selection. Feature Importance technique is utilized to obtain the highest ranked features. Reduced attributes improve the accuracy as well as decrease the computation time and prediction time. The experimental results on UNSW-NB dataset show that there is a drastic decrease in the computation time with reduced attributes compared to evaluating the model using the dataset with the entire set of attributes.


Accuracy Attributes Feature selection Machine learning Computation time 


  1. 1.
    Almseidin, M., Alzubi, M., Kovacs, S., Alkasassbeh, M.: Evaluation of machine learning algorithm for intrusion detection. Department of Information Technology, University of Miskolc, Hungary (2018)Google Scholar
  2. 2.
    Kumar, K., Batth, J.S.: Network intrusion detection with feature selection techniques using machine-learning algorithms. Int. J. Comput. Appl. 150(12), 1–13 (2016)Google Scholar
  3. 3.
    Belavagi, M.C., Muniyal, B.: Performance evaluation of supervised machine learning algorithms for intrusion detection. Procedia Comput. Sci. 89, 117–123 (2016)CrossRefGoogle Scholar
  4. 4.
    Mogal, D.G., Ghungrad, S.R., Bapusaheb, B.B.: NIDS using machine learning classifiers on UNSW-NB15 and KDDCUP99 datasets. In: IJARCCE (2017)Google Scholar
  5. 5.
    Moustafa, N., Slay, J.: The significant features of the UNSW-NB15 and the KDD99 data sets for network intrusion detection systems. In: 4th International Workshop on Building Analysis Datasets and Gathering Experience Returns for Security (BADGERS), Kyoto, Japan (2015)Google Scholar
  6. 6.
    Kumar, D., Singh, H.: A study on performance analysis of various feature selection techniques in intrusion detection systems, vol. 3, no. 6, pp. 50–54 (2015)Google Scholar
  7. 7.
    Li, G., Yan, Z., Fu, Y., Chen, H.: Data fusion for network intrusion detection: a review. Secur. Commun. Netw. 2018, 16 (2018)Google Scholar
  8. 8.
    Belouch, M., El Hadaj, S., Idhammad, M.: Performance evaluation of intrusion detection based on machine learning using Apache Spark. Procedia Comput. Sci. 127, 1–6 (2018)CrossRefGoogle Scholar
  9. 9.
    Farnaaz, N., Jabbar, M.A.: Random forest modeling for network intrusion detection system. Procedia Comput. Sci. 89, 213–217 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • N. Maajid Khan
    • 1
  • Nalina Madhav C
    • 1
  • Anjali Negi
    • 1
  • I. Sumaiya Thaseen
    • 1
    Email author
  1. 1.School of Information Technology and EngineeringVellore Institute of TechnologyVelloreIndia

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