Network Intrusion Detection Method Based on RS-LSSVM

Abstract

An intrusion detection method based on RS-LSSVM is studied in this paper. Firstly, attribute reduction algorithm based on the generalized decision table is proposed to remove the interference features and reduce the dimension of input feature space. Then the classification method based on least square support vector machine (LSSVM) is analyzed. The sample data after dimension reduction is used for LSSVM training, and the LSSVM classification model is obtained, which forms the ability of detecting unknown intrusion. Simulation results show that the proposed method can effectively remove the unnecessary features and improve the performance of network intrusion detection.

Art Image Attribute Classification Based on Support Vector Machine

“…The advantage of this feature is that the image is more robust to translation, scale change and rotation than other visual features, and it is relatively easy to extract the color features of digital image by computer. Therefore, image classification technology based on color features has been widely valued and applied in a large number of application fields [3] The selection process of artistic features is divided into two steps: (1) Visual inspection. Analyze the color, texture and shape of the two types of images, and select the features that may separate the two types.…”