Asian Journal of Computer Science and Technology (AJCST)
A Data Mining Approach for Intrusion Detection in a Computer Network
Author : M. Priya and M. RajeshwariVolume 8 No.1 Special Issue:February 2019 pp 94-97
Abstract
As activities being done on the internet keep expanding every day due to the fact that we are in the era of the information age, securing sensitive and crucial data on computer networks against malicious attacks tends to be a challenging issue. Designing effective Intrusion Detection Systems (IDSs) with maximized accuracy and low rate of false alarms is an imperative need in the world of cyber-attacks. This work was designed to employ an ensemble data mining technique for improving IDSs by carrying out some experiments using the KDD 99 intrusion dataset. Dataset was fragmented into five, representing the major categories of attacks: Normal, DOS (Denial of Service), Probing (Information gathering), R2L (Remote to Local) and U2R (User to Root). An ensemble classifier using the Stacking method with the Naïve Bayes and Multilayer perceptron algorithms as the base classifiers and J48 as the meta learner was developed. The base classifiers were also employed on the dataset individually, and performance comparison was done between individual classifiers and the ensemble classifier. A 10-fold cross validation for training and testing of data and Gain ratio technique for filtering of the dataset was adopted. Ensemble classifier maximized accuracy the most and helped in reduction of false positives of the U2R attack type.
Keywords
Intrusion Detection System, Ensemble, Stacking, Network Attacks, Data Mining
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References
[1] R. C. Summers, 1997, “Secure computing: threats and safeguards”, New York: McGraw Hill:
[2] A. Mounji, “Languages and Tools for Rule Based Distributed Intrusion Detection”, PhD Thesis, Faculties Universalities Notre-Dame delaPaix Namur, 1997.
[3] G. V. Nadiammai, S. Krishaveni, M. Hemalatha, “A comprehensive Analysis and study in intrusion detection system using data mining Techniques”, IJCA, Vol. 35, No. 8, 2011.
[4] A. Youssef and A. Emam, “Network intrusion detection using data mining and network behavior”, International Journal of Computer Science & Information Technology, IJCSIT, Vol. 3, No. 6, 2011.
[5] Iwan, Syarif Ed, Zaluska, Adam Prugel-Bennett, Gary Wills, “Application of Bagging, Boosting and Stacking to Intrusion Detection”, School of Electronics and Computer Science, University of Southampton, UK, 2012.
[6] H. Zhao, “Intrusion Detection Ensemble Algorithm based on Bagging and Neighbourhood Rough Set”, International Journal of Security and Its Applications Vol. 7, No. 5 pp.193-204, 2014.
[7] T Subbulakshmi, A Ramamoorthi, and S. M. Shalinie, “Ensemble design for intrusion detection systems”, International Journal of Computer science & Information Technology, IJCSIT, Vol. 1, No 1, 2009.
[8] S Singh and Sanjay, “An ensemble approaches for feature selection of Cyber Attack Dataset”, International Journal of Computer Science and Information Security IJCSIS, Vol. 6, No. 2, 2009.
[9] A. Borji, “Combining Heterogeneous Classifiers for Network Intrusion Detection”, Cervesato, Ed. ASIAN 2007, LNCS 4846, pp. 254-260 © Springer-VerlagBerlin Heidelberg, 2007.
[10] M. Govindarajan and R. M. Chandrasekaran, “Intrusion Detection using an Ensemble of Classification Methods”, Proceedings of the World Congress on Engineering and Computer Science, San Francisco, USA, WCECS, Vol. 1, October 24-26 2012.
[11] H Ibrahim, M. Badr, A. Shaheen, “Adaptive Layered Approach using Machine Learning Techniques with Gain Ratio for Intrusion Detection Systems”, International Journal of Computer Applications, Vol.56, No. 7, 2012.
[12] Yimin Wu, “High –dimensional Pattern Analysis in Multimedia Information Retrieval and Bioinformatics”, Doctoral Thesis, State University of New York, January 2004.