Call for Paper - February 2019 Edition
IJCA solicits original research papers for the February 2019 Edition. Last date of manuscript submission is January 21, 2019. Read More

Artificial Neural Network based System for Intrusion Detection using Clustering on Different Feature Selection

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2015
Wasima Matin Tammi, Noor Ahmed Biswas, Ziad Nasim, Khadizatul Zannat Shorna, Faisal Muhammad Shah

Wasima Matin Tammi, Noor Ahmed Biswas, Ziad Nasim, Khadizatul Zannat Shorna and Faisal Muhammad Shah. Article: Artificial Neural Network based System for Intrusion Detection using Clustering on Different Feature Selection. International Journal of Computer Applications 126(12):21-28, September 2015. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Wasima Matin Tammi and Noor Ahmed Biswas and Ziad Nasim and Khadizatul Zannat Shorna and Faisal Muhammad Shah},
	title = {Article: Artificial Neural Network based System for Intrusion Detection using Clustering on Different Feature Selection},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {126},
	number = {12},
	pages = {21-28},
	month = {September},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


Intrusion Detection System (IDS) is an example of Misuse Detection System that works for detecting malicious attacks. This can be defined as software for security management. Many researchers have proposed the Intrusion Detection System with different techniques to achieve the best accuracy. In this paper it is projected that intrusion detection system with the amalgamation of k-means clustering and artificial neural network to improve the system. To obtain a better result benchmark dataset was split into training and testing part and then cluster the dataset into five different divisions. After getting the cluster data it has been trained by the different Artificial Neural Networks functions as- Feed Forward Neural Network (FFNN), Elman Neural Network (ENN), Generalized Regression Neural Network (GRNN), Probabilistic Neural Network (PNN) and Radial Basis Neural Network (RBNN). After implementing these functions we have proposed a comparative analysis between them and choose the best accuracy rate among them. Here, it has been proved that, using the clustering technique a better accuracy rate can be found that improve the system with the best neural network functions which is the probabilistic neural network. It is also important to select efficient feature sets for better accuracy.


  1. Bouzida Y., Cuppens F., 2006, Neural networks vs. decision trees for intrusion detection, In IEEE / IST Workshop on Monitoring, Attack Detection and Mitigation.
  2. Shrivas A.K., Dewangan A. K., 2014 An Ensemble Model for Classification of Attacks with Feature Selection based on KDD99 and NSL-KDD Data Set, International Journal of Computer Applications (0975 – 8887) Vol 99 – No.15.
  3. Elhamahmy M. E., Hesham N. E. and Imane A. S., 2010 A New Approach for Evaluating Intrusion Detection System, CiiT International Journal of Artificial Intelligent Systems and Machine Learning, Vol 2, No 11
  4. Surana S. 2013 Intrusion Detection using Fuzzy Clustering and Artificial Neural Network, Advances in Neural Networks, Fuzzy Systems and Artificial Intelligence, ISBN- 978-960-474-379-7.
  5. Osoba O., Kosko B., 2013 Noise-enhanced clustering and competitive learning algorithms, Neural Networks 37 (2013) 132–140.
  6. Kumar V., Chauhan H., Panwar D., 2013 K-Means Clustering Approach to Analyze NSL-KDD Intrusion Detection Dataset, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-3, Issue-4.
  7. Wu, Junjie, Advances in K-means Clustering, A Data Mining Thinking, Springer Press, ISBN: 9783642298073
  8. Wang G., Hao J., Ma J., Huang L. 2010 A new approach to intrusion detection using Artificial Neural Networks and Fuzzy clustering, Expert system with applications, vol 37, pp. 6225-6232.
  9. Xu R., Wunsch D. C., Clustering, Wiley, IEEE Press, ISBN-10: 0470276800
  10. Sindhu S. S. S., Geetha S., Kannan A. 2012 Decision tree based light weight intrusion detection using a wrapper approach, Expert Systems with Applications 39 129–141.
  11. Gaikwad D.P., Jagtap S., Thakare K., Budhawant V. 2012 “Anomaly Based Intrusion Detection System Using Artificial Neural Network and Fuzzy Clustering”, International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Vol. 1 Issue 9.
  12. Du K. L. 2010 Clustering: A neural network approach, Expert system with applications, vol 37, pp. 6225-6232.
  13. Devaraju S., Ramakrishnan S. 2013 Detection of Accuracy for Intrusion Detection System using Neural Network classifier, International Journal of Emerging Technology and Advanced Engineering, Volume 3 Special Issue 1.
  14. Novikov D., Roman V., Yampolskiy, and Reznik, L. 2006 Artificial Intelligence Approaches For Intrusion Detection, IEEE computer society.
  15. Beghdad R. 2008 Critical Study on neural network in detecting intrusions. Computers and Security, 27(5-6)186-175.
  16. Lisehroodi M. M., Muda Z., and Yassin W. 2013 A hybrid framework based on neural network MLP and K-means Clustering for Intrusion Detection System, 4th International Conference on Computing and Informatics, ICOCI.
  17. Sakthi M., Thanamani A. S. 2013 An Enhanced K Means Clustering using Improved Hopfield Artificial Neural Network and Genetic Algorithm, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-2, Issue-3.
  18. Aneetha A.S., Bose S. 2012 The Combined Approach for Anomaly Detection using Neural network and Clustering Techniques, Computer Science & Engineering: An International Journal (CSEIJ), Vol.2, No.4
  19. Gaddam, Shekhar R., Kiran P., Vir V., & Balagani. 2007 A novel method for supervised anomaly detection by cascading K-Means clustering and ID3 decision tree learning methods, IEEE Transactions on Knowledge and Data Engineering, 19, 3
  20. Al-Subaie M. 2006 The power of sequential learning in anomaly intrusion detection, degree master thesis, Queen University, Canada.
  21. Bahrololum M., Salahi E. and Khaleghi M. 2009 Anomaly Intrusion Detection System using hybrid of Unsupervised and Supervised Neural Network, International Journal of Computer Networks & Communications (IJCNC), Vol.1,Issue No.2
  22. Siddiqui M. K., Naahid S. 2013 Analysis of KDD CUP 99 Dataset using Clustering based Data Mining, International Journal of Database Theory and Application Vol.6, No.5.
  23. Kanungo T., Mount D. M. 2002 An Efficient k-means Clustering Algorithm: Analysis and Implementation, IEEE Transactions on Pattern Analysis and Machine Intelligence  Vol: 24 ,  Issue: 7 
  24. Everitt B. S., Landau S., Leese M., Stahl D., Cluster Analysis, 5th Edition, Wiley. ISBN : 978-0-470-97844-3
  25. Venables W. N., and Ripley B. D. 2002 Modern Applied Statistics with S, Springer-Verlag.
  26. Rogas R., Neural Network- A Systematic Introduction, 3rd Edition- Springer Press, eISBN: 978-3-642-61068-4
  27. Kriesel D., A Brief Introduction to Neural Network, -Zeta 2 Edition,
  28. Kukielka P. and Kotulski Z. 2008 Analysis of different architectures of neural networks for application in intrusion detection systems, In proceeding of the international multiconference on computer science and information technology, pp. 807-811.
  29. Moradi M. and Zulkernine M. 2004 A Neural Network based system for intrusion detection and classification of attacks, Queen University, Canada.


Intrusion Detection System, K-means Clustering, Artificial Neural Network, FFNN, ENN, GRNN, PNN, RBNN