期刊名称：International Journal of Engineering and Computer Science
印刷版ISSN：2319-7242
出版年度：2014
卷号：3
期号：11
页码：9124-9127
出版社：IJECS
摘要：The intrusion detection system (IDS) is one way of protecting a computer network. This kind of technology enables users of anetwork to be aware of the incoming threats from the Internet by observing and analyzing network traffic. The proposed technique involvedfour steps, first apply DBSCAN clustering which is used to make clusters, based on this obtained clusters we trained the network with byBack Propagation algorithm. We also apply Information Gain based Feature Selection method to identify the important features of thenetwork. We trained the network once with all features and then reduced features this shows that we attain high detection rate and inefficient time. The developed network is used to identify the occurrence of various types of intrusions in the system. The performance of theproposed approach is tested using KDD Cup’99 data set available in the MIT Lincoln Labs. Simulation result shows that the proposedapproach detects the intrusions with high detection rate and low false alarm and in high efficiency in terms of time
关键词：Artificial Neural Network; supervised Learning Algorithm; Classification; DBSCAN Clustering information gain
