Performance Analysis of Tree Based Classification Algorithms for Intrusion Detection System
Intruders attack both commercial and corporate distributed systems successfully. The problem of intruders has become vital. The most effective resistance today is the use of Intrusion Detection Systems. An intrusion detection system analysis all aspects of network activities in order to identify the existence of unusual patterns that may represent a network or system attack made by intruders attempting to compromise a system. This paper brings an idea of applying data mining algorithms to the intrusion detection system. Performance of various tree based classifiers like Decision Stump, BF Tree, ID3, J48, LAD, Random Tree, REP Tree, Random Forest and Simple Cart algorithms are compared and the experimental study shows that the Random Forest algorithm outperforms than other algorithms in terms of accuracy, specificity and sensitivity and Time.
KeywordsData Mining Intrusion Detection Machine Learning Tree based Classifiers KDD Cup Dataset
Unable to display preview. Download preview PDF.
- 1.Han, J., Kamber: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufman Publishers, Elsevier Inc. (2006)Google Scholar
- 2.Banfield, R.E., Bowyer, K.W., Philip Kegelmeyer, W.: A Comparison of Decision Tree Ensemble Creation Techniques. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–18 (2006)Google Scholar
- 3.Sabhnani, M., Serpen, G.: Application of Machine Learning Algorithms to KDD Intrusion Detection Dataset within Misuse Detection Context, pp. 1–7Google Scholar
- 4.Renu Deepti, S., Loshma, G.: A Novel Data Mining Based Approach for Remote Intrusion Detection. International Journal of Computer Trends and Technology 3(3), 430–435 (2012)Google Scholar
- 5.Kumar, Y., Upendra: An efficient Intrusion Detection Based on Detecision Tree Classifier Using Feature Reduction. International Journal of Scientific and Research Publications 2(1), 1–6 (2012)Google Scholar
- 7.Gaikwad, V.S., Kulkarni, P.J.: One Versus All Classification in Network Intrusion detection using decision tree. International Journal of Scientific and Research Publications 2(3), 1–5 (2012)Google Scholar
- 9.Available on Wikipedia, http://en.wikipedia.org/wiki/Decision_Stump (last accessed on August 12)
- 11.Quinlan, J.R.: Induction of Decision Trees. Machine Learning (1), 81–106 (1986)Google Scholar
- 12.Folorunsho, O.: Comparative Study of Different Data Mining Techniques Performance in knowledge Discovery from Medical Database. International Journal of Advanced Research in Computer Science and Software Engineering 3(3), 11–15 (2013)Google Scholar
- 13.http://weka.sourceforge.net/doc/weka/classifiers/trees/RandomTree.html (last accessed on August 12)
- 16.Sharma, A.K., Sahnip, S.: A Comparative Study of Classification Algorithms for Spam Email Data Analysis. International Journal on Computer Science and Engineering 3(5), 1890–1895 (2011)Google Scholar
- 17.KDD Cup 99 intrusion Detection Data set, http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html