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Statistics and recognition for software birthmark based on clustering analysis

机译：基于聚类分析的软件胎记统计与识别

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摘要

The result of feature selection for software birthmark has a direct bearing on software recognition rate. In this paper, we apply constrained clustering to analyze the software features (SF). The within-class (homogeneous software) and between-class (heterogeneous software) distances of features are measured based on mutual information. Information gain functions and penalty functions are constructed using homogeneous and heterogeneous SF, respectively; and redundancy is measured with correlation coefficients. Then the software birthmark features with high class distinction and minimum redundancy are selected. The example of extracting and detecting framework of birthmark feature is also given. The algorithm is analyzed and compared with the similar algorithms, and it is shown the algorithm provide an effective approach for software birthmark selection and optimization.
机译：软件胎记特征选择的结果直接关系到软件识别率。在本文中，我们应用约束聚类分析软件功能（SF）。基于互信息来测量特征的类内（同类软件）和类间（异构软件）距离。信息增益函数和惩罚函数分别使用同质和异质SF构造；冗余度用相关系数来衡量。然后，选择具有较高类别区别和最小冗余的软件胎记特征。给出了胎记特征提取与检测框架的例子。对算法进行了分析和比较，表明该算法为软件胎记的选择和优化提供了有效的途径。

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