Novel Approach of Intrusion Detection Classification Deeplearning Using SVM

  • Pritesh NagarEmail author
  • Hemant Kumar Menaria
  • Manish Tiwari
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1045)


The main objective of intrusion detection systems (IDS) is to discover the dynamic and the virulent form of network traffic that simply changes according to the characteristics of the network. The IDS methodology represents a prominent developing area in the field of computer network technology and its security. Different form of IDS has been developed working on distinctive approaches. One such kind of approach where it is used is the machine learning mechanism. In the proposed methodology, an experiment is applied on the data set named as KDD-99, including its subclasses such as denial of service (DOS), other types of attacks and the class without any form of attack. Depending upon the machine learning algorithms various distinct forms of IDS have been developed which further checks the optimization-based potential features in connection with the neural network classifier for the various forms of IDS-based attacks. This approach provides a comparative study between the ANN and the optimizer-based ANN technology. The experimental analysis shows the convolution neural network with SVM show effective analysis providing accurate forms of IDS, thereby improving its detection based on individual class along with maintaining its results fundamentally.


Intrusion detection system Denial of service Artificial neural network 


  1. 1.
    Elshoush, H.T., Osman, I.M.: Alert correlation in collaborative intelligent intrusion detection systems—A survey. Appl. Soft Comput. 11(7), 4349–4365 (2011)CrossRefGoogle Scholar
  2. 2.
    Gupta, N., Srivastava, K., Sharma, A.: Reducing false positive in intrusion detection system: A survey. Int. J. Comput. Sci. Inf. Technol. 7(3), 1600–1603 (2016)Google Scholar
  3. 3.
    Mohammed, M.N., Sulaiman, N.: Intrusion detection system based on SVM for WLAN. Proc. Technol. 1, 313–317 (2012)CrossRefGoogle Scholar
  4. 4.
    Agrawal, S., Agrawal, J.: Survey on anomaly detection using data mining techniques. Proc. Comput. Sci. 60, 708–713 (2015)CrossRefGoogle Scholar
  5. 5.
    Farnaaz, N., Jabbar, M.A.: Random forest modeling for network intrusion detection system. Proc. Comput. Sci. 89, 213–217 (2016)CrossRefGoogle Scholar
  6. 6.
    Nadiammai, G.V., Hemalatha, M.: Effective approach toward intrusion detection system using data mining techniques. Egypt. Inform. J. 15(1), 37–50 (2014)CrossRefGoogle Scholar
  7. 7.
    Modi, C., Patel, D., Borisaniya, B., Patel, H., Patel, A., Rajarajan, M.: A survey of intrusion detection techniques in cloud. J. Network Comput. Appl. 36(1), 42–57 (2013)CrossRefGoogle Scholar
  8. 8.
    Vinchurkar, D.P., Reshamwala, A.: A review of intrusion detection system using neural network and machine learning (2012)Google Scholar
  9. 9.
    Jalil, K.A., Kamarudin, M.H., Masrek, M.N.: Comparison of machine learning algorithms performance in detecting network intrusion. In: 2010 International Conference on Networking and Information Technology (ICNIT), pp. 221–226. IEEE, New York (2010)Google Scholar
  10. 10.
    Siddiqui, A.K., Farooqui, T.: Improved ensemble technique based on support vector machine and neural network for intrusion detection system. Int. J. Online Sci. 3(11) (2017)Google Scholar
  11. 11.
    Ashfaq, R.A.R., He, Y., Chen, D.: Toward an efficient fuzziness based instance selection methodology for intrusion detection system. Int. J. Mach. Learn. Cybernet. 8(6), 1767–1776 (2017)CrossRefGoogle Scholar
  12. 12.
    Jabez, J., Muthukumar, B.: Intrusion Detection System (IDS): Anomaly detection using outlier detection approach. Proc. Comput. Sci. 48, 338–346 (2015). ISSN 1877-0509.
  13. 13.
    Wang, H., Jie, G., Wang, S.: An effective intrusion detection framework based on SVM with feature augmentation. Knowl.-Based Syst. 136, 130–139 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Pritesh Nagar
    • 1
    Email author
  • Hemant Kumar Menaria
    • 1
  • Manish Tiwari
    • 1
  1. 1.Geetanjali Institute of Technical StudiesUdaipurIndia

Personalised recommendations