An Efficient Extreme Learning Machine Based Intrusion Detection System
W. Sylvia Lilly Jebarani, Mepco Schlenk Engineering college; K. Janaki ,Mepco Schlenk Engineering college; R. Anupriya ,Mepco Schlenk Engineering college
Network traffic profiling, OS-ELM
This paper presents an intrusion detection technique based on online sequential extreme learning machine. For performance evaluation, KDDCUP99 dataset is used. In this paper, we use three feature selection techniques – filtered subset evaluation, CFS subset evaluation and consistency subset evaluation to eliminate redundant features. Two network traffic profiling techniques are used. Alpha profiling is done to reduce time complexity and beta profiling is used to remove redundant connection records and hence reduce the size of dataset
-
[1] Adetunmbi A.Olusola., Adeola S.Oladele. and Daramola O.Abosede, “Analysis of KDD ’99 Intrusion Detection Dataset for Selection of Relevance Features,” World Congress on Engineering and Computer Science 2010, Vol I.
[2] Singh.R.,Kumar, H., &Singla. R. K(2014), “TOPSIS based multi-criteria decision making of feature selection techniques for network traffic dataset”, International Journal of Engineering and Technology,5(6),4598-4604.
[3] Chia-Ming Wang, Yin-Fu Huang, “Evolutionary-based feature selection approaches with new criteria for data mining: A case study of credit approval data”, April 2009.
[4] Ester,M., Kriegel, H.P., Sander,J.,&Xu,X.(1996),”A Density-based algorithm for discovery clusters in large spatial databases with noise”, Second International Conference on Knowledge Discovery and data Mining(pp.226-231).
[5] S. Revathi., A. Malathi., “A Detailed Analysis on NSL-KDD Dataset Using Various Machine Learning Techniques for Intrusion Detection”, International Journal of Engineering & Technology, Vol. 2.
[6] KDDCup dataset (1999). http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.
[7] Matlab Language of Technical Computing (2014). http://in.mathworks.com/products/matlab/.
[8] Weka 3.6.9: Data Mining Software, http://www.cs.waikato.ac.nz/ml/weka/..
Paper ID: GRDCF002092
Published in: Conference : International Conference on Innovations in Engineering and Technology (ICIET - 2016)
Page(s): 297 - 301
Published in: Conference : International Conference on Innovations in Engineering and Technology (ICIET - 2016)
Page(s): 297 - 301