CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
Reseach Article

AI based Hybrid Ensemble Technique for Network Security

Published on September 2016 by Indubala, Yogesh Kumar
International Conference on Advances in Emerging Technology
Foundation of Computer Science USA
ICAET2016 - Number 8
September 2016
Authors: Indubala, Yogesh Kumar

Indubala, Yogesh Kumar . AI based Hybrid Ensemble Technique for Network Security. International Conference on Advances in Emerging Technology. ICAET2016, 8 (September 2016), 1-10.

author = { Indubala, Yogesh Kumar },
title = { AI based Hybrid Ensemble Technique for Network Security },
journal = { International Conference on Advances in Emerging Technology },
issue_date = { September 2016 },
volume = { ICAET2016 },
number = { 8 },
month = { September },
year = { 2016 },
issn = 0975-8887,
pages = { 1-10 },
numpages = 10,
url = { /proceedings/icaet2016/number8/25924-t119/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Proceeding Article
%1 International Conference on Advances in Emerging Technology
%A Indubala
%A Yogesh Kumar
%T AI based Hybrid Ensemble Technique for Network Security
%J International Conference on Advances in Emerging Technology
%@ 0975-8887
%V ICAET2016
%N 8
%P 1-10
%D 2016
%I International Journal of Computer Applications

Due to excessive use of internet the problem of intrusion is also increased. So, to detect the intrusion in the network traffic, various AI based intrusion detection techniques are used but there is no such technique is available which is used for detecting the network attacks or monitors system activities for malicious activities and produces reports to a management station that can detect various types of network attacks with high accuracy. So the idea of this research paper is to find promising AI based method which classify each type of network traffic class and combine them by proposing an effective combination technique i. e. ensemble technique which can detect all network attacks, so as to increase the overall accuracy and performance of the IDS.

  1. A. A. Olusola, A. S. Oladele, and D. O. Abosede, "Analysis of kdd99 intrusion detection dataset for selection of relevance features," in Proceedings of the World Congress on Engineering and Computer Science, vol. 1, (2010), pp. 20–22.
  2. A. H. M. Ragab, A. Y. Noaman, A. S. Al-Ghamdi, and A. I. Madbouly, "A comparative analysis of classification algorithms for students college enrollment approval using data mining," in Proceedings of the 2014 Workshop on Interaction Design in Educational Environments. ACM, (2014), p. 106.
  3. A. H. Wahbeh, Q. A. Al-Radaideh, M. N. Al-Kabi, and E. M. Al-Shawakfa, "A Comparison Study between Data Mining Tools over some Classification Methods", (IJACSA) International Journal of Advanced Computer Science and Applications,(2011).
  4. A. Lazarevic, L. Ert¨oz, V. Kumar, A. Ozgur, and J. Srivastava, "A comparative study of anomaly detection schemes in network intrusion detection. " in SDM. SIAM, (2003), pp. 25–36.
  5. A. Satheesh, R. Patel, ¬¬¬¬¬¬¬¬"Dynamic Nearest Neighbours Classifier For Integrated Data Using Object Oriented Concept Generalization", IJSSST, Vol. 11, No. 1,(2010).
  6. D. L. AL-Nabi, S. S. Ahmed,"Survey on Classification Algorithms for DataMining:( Comparison and Evaluation)", Computer Engineering and Intelligent Systems, ISSN 2222-1719 (Paper) ISSN 2222-2863 (Online), Vol. 4, No. 8, (2013).
  7. D. TIGABU, "Constructing predictive model for network intrusion detection. "
  8. G. Kumar and K. Kumar, "The use of artificial-intelligence-based ensembles for intrusion detection: a review," Applied Computational Intelligence and Soft Computing, p. 21, (2012).
  9. H. A. Nguyen and D. Choi, "Application of data mining to network intrusion detection: classifier selection model," Springer, (2008), pp. 399–408.
  10. J. Zhang and M. Zulkernine, "Network intrusion detection using random forests. " in PST. Citeseer, (2005).
  11. K. H. Raviya, BirenGajjar, "Performance Evaluation of Different Data Mining Classification Algorithm Using WEKA", ISSN - 2250-1991, Volume 2, Issue 1 , January (2013).
  12. K. Kumar, G. Kumar, and Y. Kumar, "Feature selection approach for intrusion detection system. "
  13. L. M. Ibrahim, D. T. Basheer, and M. S. Mahmod, "A comparison study for intrusion database (kdd99, nsl-kdd) based on self organization map (som) artificial neural network," Journal of Engineering Science and Technology, vol. 8, no. 1, pp. 107–119, (2013).
  14. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, "The weka data mining software: an update," ACM SIGKDD explorations newsletter, vol. 11, no. 1, pp. 10–18, (2009).
  15. M. Revathi and T. Ramesh, "Network intrusion detection system us-ing reduced dimensionality," Indian Journal of Computer Science and Engineering (IJCSE), vol. 2, no. 1, pp. 61–67, (2011).
  16. M. Sharma, S. K. Sharma, "Generalized K-Nearest Neighbour Algorithm- A Predicting Tool", International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 11, November (2013).
  17. O. Depren, M. Topallar, E. Anarim, and M. K. Ciliz, "An intelligent intrusion detection system (ids) for anomaly and misuse detection in computer networks," Expert systems with Applications, vol. 29, no. 4, pp. 713–722, (2005).
  18. Oliver Sutton, "Introduction to k nearest Neighbour Classification", Februar 2012).
  19. O. Veksler, "Machine Learning in computer vision",(2008).
  20. P. De Boer and M. Pels, "Host-based intrusion detection systems," Amsterdam University, (2005).
  21. P. Srinivasulu, D. Nagaraju, P. R. Kumar, and K. N. Rao, "Classifying the network intrusion attacks using data mining classification methods and their performance comparison," International Journal of Computer Science and Network Security, vol. 9, no. 6, pp. 11–18, (2009).
  22. P. T. B. Fomby, "K-Nearest Neighbors Algorithm: Prediction and Classification", Department of Economics, Southern Methodist University, Dallas, TX 75275, February (2008).
  23. S. Bishnoi, "Comparison of classification techniques", ISSN 2231-4334, IJRIM, Volume 1, Issue 2 (June, 2011).
  24. S. Chebrolu, A. Abraham, and J. P. Thomas, "Feature deduction and ensemble design of intrusion detection systems," Computers & Security, vol. 24, no. 4, pp. 295–307, (2005).
  25. S. Mukkamala, A. H. Sung, and A. Abraham, "Intrusion detection using an ensemble of intelligent paradigms," Journal of network and computer applications, vol. 28, no. 2, pp. 167–182, (2005).
  26. S. Neelamegam, Dr. E. Ramaraj, "Classification algorithm in Data mining", International Journal of P2P Network Trends and Technology (IJPTT) – Volume 4 , Issue 8,(Sep 2013).
  27. S. Pandya, Dr. P. V. Virparia, "Comparing the Applications of Various Algorithms of Classification Technique of Data Mining in an Indian University to Uncover Hidden Patterns", International Journal of Advanced Research in Computer Science and Software Engineering, ISSN: 2277 128X, Volume 3, Issue 5, (May 2013).
  28. S. Thaseen and C. A. Kumar, "An analysis of supervised tree based classifiers for intrusion detection system," in Pattern Recognition, Informatics and Mobile Engineering (PRIME), 2013 International Conference on. IEEE, (2013), pp. 294–299.
  29. S. X. Wu and W. Banzhaf, "The use of computational intelligence in intrusion detection systems: A review,"vol. 10, no. 1, pp. 1–35, (2010).
  30. T. N. Phyu,"Survey of Classification Techniques in Data Mining", Proceedings of the International Multi Conference of Engineers and Computer Scientists 2009, Vol 1, IMECS 2009, March 18 - 20, (2009), Hong Kong.
  31. V. Kumar, H. Chauhan, and D. Panwar, "K-means clustering approach to analyzensl-kdd intrusion detection dataset," International Journal of Soft Computing and Engineering (IJSCE) ISSN, pp. 2231–2307,( 2013).
  32. W. Hu, W. Hu, and S. Maybank, "Adaboost-based algorithm for network intrusion detection," Systems, Man, and Cybernetics, Part B: Cybernet-ics, IEEE Transactions on, vol. 38, no. 2, pp. 577–583, (2008).
  33. Y. B. Bhavsar and K. C. Waghmare, "Intrusion detection system using data mining technique: Support vector machine," International Journal of Emerging Technology and Advanced Engineering, vol. 3, no. 3, pp. 581–586, (2013).
  34. Y. Kumar and I. Bala, "Identify Promising Classifiers for Each Type of Attack Class" Fourth International Conference on Advances in Computer Science and Application, Grenze Scientific society, CSA-2015,(2015).
  35. Y. Kumar and I. Bala, "Comparative analysis of various data mining classification algorithms", 3rd International Conference on Advancements in Engineering & Technology (ICAET-2015), (March 2015).
Index Terms

Computer Science
Information Sciences


Tp Rate Fp Rate Precision F-measure Roc Area