关键词: 入侵检测系统, KDD数据集, 支持向量机, 核函数, 网格搜索

Abstract: To enhance the approximation and generalization ability of intrusion detection system, theoretical framework of multiple classifiers is analyzed, and factors such as training data pretreatment, cross-validation time and intrusion detection model accuracy is also taken into consideration. In order to get optimal parameters rapidly, a new approach based on grid search is presented. The KDD dataset is mapped into a high-dimensional feature space via the method for intrusion detection based on support vector machine. Different algorithms are applied to optimize the related parameters for kernel function. By using improved grid search method, the acquired parameter has relatively obvious time superiority. The experimental results prove that the classification accuracy and efficiency are improved.

Key words: intrusion detection system, KDD dataset, support vector machine, kernel function, grid search