Performance Analysis of various classifiers using Benchmark Datasets in Weka tools

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2017 by IJETT Journal
Volume-47 Number-5
Year of Publication : 2017
Authors : Nishi Rani, Ravindra Kr. Purwar
DOI :  10.14445/22315381/IJETT-V47P247


Nishi Rani, Ravindra Kr. Purwar "Performance Analysis of various classifiers using Benchmark Datasets in Weka tools", International Journal of Engineering Trends and Technology (IJETT), V47(5),290-294 May 2017. ISSN:2231-5381. published by seventh sense research group

Intrusion occurs in the network due to redundant and irrelevant data that cause problem in network traffic classification. These kinds of data slow down the network and create difficulties in detecting cyber attacks. Intrusion detection system monitors the network for malicious activities. For network intrusion detection many data mining and machine learning techniques exist in literature but their efficiency has always remain a challenge. In this paper, various classification techniques of weka tool have been studied over a number of datasets like KDD cup 99 dataset, NSL KDD dataset and Kyoto 2006 dataset which can reflect current network stages. KDD cup 99 dataset contains 42 features and can classify intrusion in five classes, NSL KDD is the filtered version of KDD and able to classify intrusion in two classes and Kyoto 2006 data set contains labels as normal (no attack), attack (known attack) and unknown attack which can reflect current stages of the network. We have analyzed the performance of the classification techniques with respect to time and accuracy.


[1] Altman, N.S. (1992), An introduction to kernel and nearestneighbor nonparametric regression. The American Statistician. 46 (3) : 175-185. DOI: 10. 1080/00031305. 1992. 10475879.
[2] Pearl, Judea (2000), Casuality:Models, Reasoning, and Inference. Cambridge University Press. ISBN 0-521-77362-8. OCLC 42291253.
[3] Russell, Stuart, Norvig, Peter (2003) [1995], Artificial Intelligence : A Modern Approach(2nd ed.), Prentice Hall. ISBN 978-0137903955.
[4] Quinlan, J.R. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, 1993.
[5] Wei Pan and Weihua Li, A hybrid neural network approach to the classification of Novel attacks for intrusion detection, ISPA 2005 LNCS 3758 no 564-575, 2005.
[6] Jungsuk Song, Hiroki Takakura and Yasuo Okabe, Cooperation of Intelligent Honeypots to Detect Unknown Malicious Codes, WOMBAT workshop on Information Security Threat Data Exchange (WISTDE 2008), The IEEE CS Press, Amsterdam, Netherlands, 21-22 April 2008.
[7] Top 10 Algorithms in Data Mining. Knowl Inf syst (2008) 14:1-37. DOI 10.1007/s10115-007-0114-2.
[8] Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome (2008). The Elements of statistical learning (2nd ed.). Springer. ISBN 0- 387-95284-5.
[9] Naeem Seliya, Clustering based network intrusion detection, International Journal of Reliability, Quality and Safety Engineering, Vol. 14, No. 2 (2007) 169-187.
[10] Mahbod Tavallaee, Ebrahim Bagheri, Wei Lu, A Detailed analysis of the KDD cup 99 dataset, Proceedings of the 2009 IEEE Symposium on Computational Intelligence in Security and Defense Applications (CISDA 2009)
[11] C. Kolias, G. Kambourakis and M. Maragoudakis, Swarm intelligence in intrusion detection: A survey, Computers and Security 30 (2011) 625-642.
[12] Chang, Chih-Chung, Lin, Chih-Jen (2011), LIBSVM :A Library for support vector machines. ACM Transactions on Intelligent Systems and Technology. 2(3)
[13] Yogendra Kumar Jain and Upendra, An efficient intrusion detection based on decision tree classifier using feature reduction. International Journal of Scientific and Research Publications, vol. 2, issue 1, ISSN 2250-3153, Jan 2012.
[14] S. Revathi and Dr. A. Malathi, A Detailed analysis on NSLKDD dataset using various machine learning techniques for Intrusion detection, International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Vol. 2 Issue 12, December-2013
[15] M.A. Ambusaidi, Priyadarsi Nanda, Building an intrusion detection system using a filter-based feature selection algorithm, IEEE transactions on computers, Vol no 65, November 2014.
[16] M.A. Ambusaidi, Xiangjian He* and Priyadarsi Nanda, Unsupervised feature selection method for intrusion detection system, IEEE Trustcom/BigDataSE/ISPA, 2015.
[17] Shailendra Sahu, B M Mehtre, Network intrusion detection system using J48 decision tree, International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2015. Hyderabad, India. 10-13 August, 2015.
[18] Jungsuk SONG, Hiroki Takakura and Yasuo Okabe, Description of Kyoto University Benchmark Data, Academic Center for Computing and Media Studies (ACCMS), Kyoto University

Intrusion detection, KDD cup 99 dataset, NSL KDD dataset, Kyoto 2006 dataset, weka.