A CFS–DNN-Based Intrusion Detection System

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 462)


The Internet communications are being developed to a great extent. With this development, an enormous amount of data is being generated. However, this data is not always secured. Intruders are always trying to misuse this data and gain unauthorized access, and hence, network security is also being compromised. An Intrusion Detection System (IDS) provides an efficient way to handle this. In this paper, an efficient IDS has been proposed which uses the NSL-KDD dataset which is a high-dimensional dataset. The dataset contains a large number of records, labeled as attack or normal. Correlation-based Feature Selection (CFS) method is chosen to select relevant and important features from the dataset for reducing the overall runtime of the proposed model, and a Deep Neural Network (DNN) classifier is used to examine if a record is normal or an attack. We tested our model using model validation and also compared the results with other existing models.


Feature selection IDS CFS DNN Model validation 


  1. 1.
    Belavagi C. M., and Muniyal B.: Performance Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection. Twelfth International Multi-Conference on Information Processing-2016 (IMCIP-2016), pp. 117–123, (2016).Google Scholar
  2. 2.
    Denning., and D. E.: An intrusion detection model. IEEE Transaction on Software Engineering. Software Engineering 13(2), pp. 222–232, (1987).Google Scholar
  3. 3.
    Singh S. K., Chaurasia N., and Sharma P.: Concept and proposed architecture of Hybrid Intrusion Detection System using data mining. International Journal of Engineering and Advanced Technology (IJEAT)., vol. 2, pp. 274–276, (2013).Google Scholar
  4. 4.
    Ubhale P. R., and Sahu A. M.: Securing Cloud Computing Environment by means of Intrusion Detection and Prevention Systems (IDPS). International Journal of Computer Science and Management Research., vol. 2, pp. 2430–2435, (2013).Google Scholar
  5. 5.
    Jennifer G.. D., and Carla E. B.: Feature Selection for Unsupervised Learning, Journal of Machine Learning Research 5, pp. 845–889, (2004).Google Scholar
  6. 6.
    Yu L., and Liu H.: Efficient Feature Selection via Analysis of Relevance and Redundancy. Journal of Machine Learning Research 5., pp. 1205–1224, (2004).Google Scholar
  7. 7.
    Datti R., and Verma B.: Feature Reduction for Intrusion Detection Using Linear Discriminant Analysis. International Journal on Computer Science and Engineering (IJCSE)., vol. 2, pp. 1072–1078, (2010).Google Scholar
  8. 8.
    Aghdam M. H., and Kabiri P.: Feature selection for intrusion detection system using ant colony optimization. International Journal of Network Security., pp. 420–432, (2016).Google Scholar
  9. 9.
    Jha J., and Ragha L.: Intrusion Detection System using Support Vector Machine, International Journal of Applied Information Systems., pp. 25–30, (2103).Google Scholar
  10. 10.
    NSL-KDD dataset for network-based intrusion detection system. Available on:, (2009).
  11. 11.
    Tavallaee M., Bagheri E., Lu W., and Ali A. G..: A Detailed Analysis of the KDD CUP 99 Data Set. IEEE Symposium on Computational Intelligence for Security and Defense Applications, CISDA 2009., pp. 53–58, (2009).Google Scholar
  12. 12.
    MIT Lincoln Labs, 1998 DARPA Intrusion Detection Evaluation. Available on: index.html, February 2008.
  13. 13.
    Battiti R.: Using Mutual Information for selecting features in Supervised Neural Net Learning. IEEE Transactions on Neural Networks., vol. 5, pp. 537–550, (1994).Google Scholar
  14. 14.
    Hall M. A.: Correlation-based Feature Selection for Machine Learning, Department of Computer Science, The University of Waikato, New Zealand, (1999).Google Scholar
  15. 15.
    Goodfellow I., Bengio Y., and Courville A.: Deep Learning, MIT Press (2016) available on
  16. 16.
    Adam Optimizer (Keras official documentation) available on:
  17. 17.
    Han J., Kamber M., and Pei J.: Data Mining: Concepts and Techniques (3rd Edition). USA, MA: Morgan Kaufmann Publishers, pp. 359–360, (2012).Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Netaji Subhash Engineering CollegeKolkataIndia

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