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作 者:王槐源[1]
机构地区:[1]琼州学院电子信息工程学院,海南三亚572022
出 处:《科技通报》2015年第2期227-229,246,共4页Bulletin of Science and Technology
摘 要:在对波段性网络入侵差异化特征进行提取的过程中,会出现入侵差异化特征伪装程度逐渐升高的情况,导致传统的基于敏感性数据挖掘的波段性网络入侵差异化特征提取方法,由于不能有效区分入侵特征与正常特征,无法有效实现波段性网络入侵差异化特征的有效提取,提出一种基于支持向量机的波段性网络入侵差异化特征提取模型,获取不确定入侵中波段性网络节点的差异化特征以及入侵节点,给出两种不确定入侵中波段性网络节点的差异化特征训练数据样本集,通过非线性映射将数据样本集从原空间映射到高维特征空间中,得到高维特征空间中最优线性分类面,采用支持向量机求解该分类面的优化解,使用网格搜索法,通过调整错分惩罚因子与核宽度,分别训练不同的支持向量机,获取泛化能力最强的参数组合,完成入侵中波段性网络节点的差异化特征所对应的数据集的核参数优化和分类,实现波段性网络入侵节点的差异化特征的有效提取。仿真实验结果表明,所提方法具有很高的准确性及有效性。In the band sexual network intrusion differentiation characteristics of the process of extraction, there will be a degree of invasion of differentiation characteristics of camouflage gradually rise, lead to the traditional data mining based on the sensitivity of band network intrusion differentiation feature extraction methods, because cannot effectively distinguish between the invasion features with normal, unable to effectively realize effective extraction of band network intrusion differentiation characteristics, puts forward a kind of based on support vector machine(SVM) band network intrusion differentiation feature extraction model, obtain the invasion of uncertain band sexual differentiation characteristics of network nodes,and invasion of node, given two kinds of uncertainty in the invasion of band sexual differentiation characteristics of network node training data sample set, through nonlinear mapping data sample set from the original space is mapped to high-dimensional feature space, get the optimal linear classification in the high dimensional feature space, solve the classification based on support vector machine the optimization solution, using the grid search method, by adjusting the wrong points penalty factor and kernel width, different training support vector machine(SVM) respectively, to obtain the parameters of the strongest generalization ability combination, complete invasion of band sexual differentiation characteristics of the network node nuclear parameter optimization and classification of the data set, realize band of sexual differentiation characteristics of the network intrusion node extraction effectively. The simulation results show that the proposed method has high accuracy and effectiveness.
分 类 号:TP144[自动化与计算机技术—控制理论与控制工程]
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