Web应用程序天然存在多种漏洞,使得跨站脚本(Cross-site scripting,XSS)攻击实施简单但能产生较大危害,如何快速准确检测出XSS攻击是Web应用程序面临的一个难题。对此,基于单分类支持向量机(One Class Support Vector Machine,OCSVM)分...Web应用程序天然存在多种漏洞,使得跨站脚本(Cross-site scripting,XSS)攻击实施简单但能产生较大危害,如何快速准确检测出XSS攻击是Web应用程序面临的一个难题。对此,基于单分类支持向量机(One Class Support Vector Machine,OCSVM)分类器提出一个新的XSS攻击检测模型。采用基于TF-IDF算法的特征向量化方法,对XSS攻击样本进行分析;基于单分类模型,对样本数据进行训练及测试;从准确率、召回率及加权调和平均数三个指标对该模型的检测效果进行评价。实验结果表明,与现有检测方法相比,该检测模型具有更好的检测效果。展开更多
The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a se...The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables. PCA also is a tool to reduce multidimensional data to lower dimensions while retaining most of the information. It covers standard deviation, covariance, and eigenvectors. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.展开更多
文摘Web应用程序天然存在多种漏洞,使得跨站脚本(Cross-site scripting,XSS)攻击实施简单但能产生较大危害,如何快速准确检测出XSS攻击是Web应用程序面临的一个难题。对此,基于单分类支持向量机(One Class Support Vector Machine,OCSVM)分类器提出一个新的XSS攻击检测模型。采用基于TF-IDF算法的特征向量化方法,对XSS攻击样本进行分析;基于单分类模型,对样本数据进行训练及测试;从准确率、召回率及加权调和平均数三个指标对该模型的检测效果进行评价。实验结果表明,与现有检测方法相比,该检测模型具有更好的检测效果。
文摘The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables. PCA also is a tool to reduce multidimensional data to lower dimensions while retaining most of the information. It covers standard deviation, covariance, and eigenvectors. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.
