An Efficient Extreme Learning Machine Based Intrusion Detection System

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 Citation: W. Sylvia Lilly Jebarani, Mepco Schlenk Engineering college; K. Janaki ,Mepco Schlenk Engineering college; R. Anupriya ,Mepco Schlenk Engineering college. "An Efficient Extreme Learning Machine Based Intrusion Detection System." Global Research and Development Journal For Engineering : 297 - 301.