期刊名称：International Journal of Information and Network Security (IJINS)
印刷版ISSN：2089-3299
出版年度：2012
卷号：1
期号：1
页码：28-36
DOI：10.11591/ijins.v1i1.339
语种：English
出版社：Institute of Advanced Engineering and Science
摘要：With the rapidly growing and wide spread use of computer networks, the number of new attacks and malicious has grown widely. Intrusion Detection System can identify the attacks and protect the systems successfully. But, performance of IDS related to feature extraction and selection phases. In this paper, we proposed new feature transformation to overcome this weakness. For this aim, we combined LDA and PCA as feature transformation and RBF Neural Network as classifier. RBF Neural Net (RBF-NN) has a high speed in classification and low computational costs. Hence, the proposed method can be used in real time systems. Our results on KDDCUP99 shows our proposed method have better performance related to other feature transformation methods such as LDA, PCA, Kernel Discriminant Analysis (KDA) and Local Linear Embedding (LLE).
