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The source data of intrusion detection system (IDS) are characteristic of heavy-flow, high-dimension and nonlinearity. A frequent problem in IDS is the choice of the right features that give rise to compact and concise representations of the network data; the other is how to improve the detection efficiency and accuracy of IDS under the small sample conditions. In order to delete the redundant and...
Security of computers and the networks that connect them is increasingly becoming of great significance. As an effect, building effective intrusion detection models with good accuracy and real-time performance are essential. In this paper we propose a new data mining based technique for intrusion detection using Cost-sensitive classification and Support Vector Machines. We introduced an algorithm...
Intrusion Detection System (IDS) is an important and necessary component in ensuring network security and protecting network resources and infrastructures. In this paper, we effectively introduced intrusion detection system by using Principal Component Analysis (PCA) with Support Vector Machines (SVMs) as an approach to select the optimum feature subset. We verify the effectiveness and the feasibility...
When collecting network connection information, we can not obtain a complete data set at once, which result in SVM training insufficiently and high error rate of prediction. To solve this problem, this paper proposes a new method that combines support vector machine with clustering algorithm, based on analyzing the relation between boundary support vectors and KKT condition. In the method, firstly,...
The main function of IDS (intrusion detection system) is to protect the system, analyze and predict the behaviors of users. Then these behaviors will be considered an attack or a normal behavior. Though IDS has been developed for many years, the large number of return alert messages makes managers maintain system inefficiently. In this paper, we use RST (Rough Set Theory) and SVM (Support Vector Machine)...
When the same data are detected and classified with different classifiers, there will be inconsistencies in the results. This shows that different factors cause the classifierspsila detection accuracy not alike. In this study, the proposed methods were verified with KDDCUPpsila99 data, and data fusion (DF) using five feature selection methods (discriminant analysis, DA; principal component analysis,...
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