Classification of Intrusion Detection Using Data Mining Techniques

  • Roma Sahani
  • Shatabdinalini
  • Chinmayee Rout
  • J. Chandrakanta Badajena
  • Ajay Kumar Jena
  • Himansu Das
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 710)


Nowadays, Internet became a common way for communication as well as a key path for business. Due to the rapid use of Internet, its security aspect is turn more important day by day for which various network intrusion detection systems (NIDSs) are used to protect network data as well as protect the overall network from various attacks. Various intrusion detection systems (IDSs) are placed in different positions of network to protect it. There are various ways by which intrusion detection system can be implemented from which decision tree approach is most commonly used. It provides the easiest way to identify the most corrected field to select, manage, and make proper decision about their identification from a large dataset. This paper focuses to identify normal and attack data present in the network with the help of C4.5 algorithm which is one of the decisions tree techniques, and also it helps to improve the IDS system to identify the type of attacks present in a network. Experimentation is performed on KDD-99 dataset having number of features and different class of normal and attack type data.


NIDS Decision tree C4.5 KDD-99 


  1. 1.
    Barbara, Daniel, et al.: ADAM: Detecting intrusions by data mining. In Proceedings of the IEEE Workshop on Information Assurance and Security. (2001): 11–16.Google Scholar
  2. 2.
    Swamy, K.V.R., and K.S. Vijaya Lakshmi: Network intrusion detection using improved decision tree algorithm. International Journal of Computer Science and Information Security 10.8 (2012): 4971–4975.Google Scholar
  3. 3.
    Farid, Dewan Md, et al.: “Attacks classification in adaptive intrusion detection using decision tree.” World Academy of Science, Engineering and Technology 63 (2010): 86–90.Google Scholar
  4. 4.
  5. 5.
    Sarkar, Sutapa: High Performance Network Security Using NIDS Approach. International Journal of Information Technology and Computer Science (IJITCS) 6.7 (2014): 47–55.Google Scholar
  6. 6.
    Das, Niva, and Tanmoy Sarkar: Survey on host and network based intrusion Detection System. Int. Journal of Advanced Networking and Applications 6.2 (2014): 2266–2269.Google Scholar
  7. 7.
  8. 8.
    Paliwal, Swati, and Ravindra Gupta: Denial-of-service, probing & remote to user (R2L) attack detection using genetic algorithm. International Journal of Computer Applications 60.19 (2012): 57–62.Google Scholar
  9. 9.
    Kumar, Sandeep, and Satbir Jain: “Intrusion detection and classification using Improved ID3 algorithm of data mining.” International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) 1.5 (2012): 352–356.Google Scholar
  10. 10.
    Moon, Daesung, et al.: DTB-IDS: An intrusion detection system based on decision Tree using behavior analysis for preventing APT attacks. The Journal of supercomputing (2015): 1–15.Google Scholar
  11. 11.
    P Sarkhel, Himansu Das, and L K Vashishtha, “Task Scheduling Algorithms in Cloud Environment”, In 3rd International Conference on Computational Intelligence in Data Mining, Springer India, 2017.Google Scholar
  12. 12.
    I Kar, RNR Parida, Himansu Das, “Energy Aware Scheduling using Genetic Algorithm in Cloud Data Centers” in International Conference on Electrical, Electronics, and Optimization Techniques, IEEE, 2016.Google Scholar
  13. 13.
    Himansu Das, A K Jena, P K Rath, B Muduli, S R Das, “Grid Computing Based Performance Analysis of Power System: A Graph Theoretic Approach”, in International Conference on Intelligent Computing, Communication & Devices, Springer India, 2015, pp. 259–266.Google Scholar
  14. 14.
    Himansu Das, G S Panda, B Muduli, and P K Rath. “The Complex Network Analysis of Power Grid: A Case Study of the West Bengal Power Network.” In International Conference on Advanced Computing, Springer India, 2014, pp. 17–29.Google Scholar
  15. 15.
    KHK Reddy, Himansu Das, D S Roy, “A Data Aware Scheme for Scheduling Big-Data Applications with SAVANNA Hadoop”, in Futures of Network, CRC Press, 2017.Google Scholar
  16. 16.
    Panigrahi, C R, M Tiwary, B Pati, and Himansu Das., “Big Data and Cyber Foraging: Future Scope and Challenges.” In Techniques and Environments for Big Data Analysis, Springer India, 2016, pp. 75–100.Google Scholar
  17. 17.
    Himansu Das, D.S.Roy, “A Grid Computing Service for Power System Monitoring,” International Journal of Computer Applications (IJCA), 2013, Vol. 62 No. 20, pp 1–7Google Scholar
  18. 18.
    Himansu Das, Bighnaraj Naik, Bibudendu Pati, and Chhabi Rani Panigrahi, “A Survey on Virtual Sensor Networks Framework,” International Journal of Grid & Distributed Computing (IJGDC), 2014, Vol. 7 no. 5, pp 121–130Google Scholar
  19. 19.
    Himansu Das, D.S.Roy, “The Topological Structure of the Odisha Power Grid: A Complex Network Analysis”, in International Journal of Mechanical Engineering and Computer Applications (IJMCA), 2013, Vol.1 Issue 1, pp 12–18Google Scholar
  20. 20.
    Rathee, Anju, and Robin Prakash Mathur: Survey on decision tree classification algorithms for the evaluation of student performance. International Journal of Computers & Technology 4.2a1 (2013): 244–247.Google Scholar
  21. 21.
    Patel, B.R. and Kushik K.R.: A survey on decision tree algorithm for classification. Int. Journal of Engineering Development and Research 2.1 (2014): 1–5.Google Scholar
  22. 22.
  23. 23.
    Das, Himansu, Ajay Kumar Jena, Janmenjoy Nayak, Bighnaraj Naik, and H. S. Behera. “A novel PSO based back propagation learning-MLP (PSO-BP-MLP) for classification.” In Computational Intelligence in Data Mining-Volume 2, pp. 461–471. Springer, New Delhi, (2015).Google Scholar
  24. 24.
    DARPA Intrusion Detection Evaluation KDD dataset, December 2016.
  25. 25.
  26. 26.
    Quinlan, J. Ross: Induction of decision trees. Machine learning 1.1 (1986): 81–106.Google Scholar
  27. 27.
    Recent attack Presents over internet, May 2017.
  28. 28.
    Rai, Kajal, M. Syamala Devi, and Ajay Guleria: Decision Tree Based Algorithm for Intrusion Detection, Int. Journal of Advanced Networking and Applications 7.4 (2016): 2828–2834.Google Scholar
  29. 29.
    Phutane, Ms Trupti, and Apashabi Pathan: Intrusion detection system using decision tree and apriori algorithm. Journal of Computer Engineering and Technology 6.7 (2015): 09–18.Google Scholar
  30. 30.
    Shon Nadiammai, G.V., and M. Hemalatha: Effective approach toward Intrusion Detection System using data mining techniques. Egyptian Informatics Journal 15.1(2014): 37–50.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Roma Sahani
    • 1
  • Shatabdinalini
    • 1
  • Chinmayee Rout
    • 2
  • J. Chandrakanta Badajena
    • 1
  • Ajay Kumar Jena
    • 3
  • Himansu Das
    • 3
  1. 1.Department of Information TechnologyCollege of Engineering & TechnologyBhubaneswarIndia
  2. 2.Department of Computer Science and EngineeringAjay Binay Institute of TechnologyCuttackIndia
  3. 3.School of Computer EngineeringKIIT Deemed to be UniversityBhubaneswarIndia

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