基于粗糙集和贝叶斯分类器的病毒程序检测
VirusDetection Based on Rough Set and Bayes C lassifier
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摘要: 在病毒程序检测中将粗糙集与贝叶斯分类器相结合.该方法在粗糙集属性约简的基础上,综合考虑了条 件属性和决策属性的依赖性以及条件属性间的依赖性对约简的影响.通过基于依赖性的属性约简,减少对属性 变量间独立性的限制,发挥贝叶斯分类器的鲁棒性潜能,优化贝叶斯分类器的特性.实验结果表明,检测率达到 97. 88%,正确率为97. 16%,明显高于传统的基于特征和RIPPER的方法,也高于多贝叶斯方法;虚警率为 5. 19%,也比上述所有方法均有所降低.Abstract: Rough set and Bayes classifier were integrated in virus detection. On the basis of the feature reduction algorithm of rough se,t the proposed method takes synthetically into account the influences of the dependency of condition features and decision-making features and those of the dependency among condition features on reduction. W ith the method, the limitation on the independence among attribute variables are relaxed, the potential robust properties ofBayes classifier are utilized and its performances are improved. Experiment results show that the detection rate is 97. 88%, and the accuracy is 97. 16%, superior to the signature-based method, RIPPER(repeated incrementalpruning to produce erroe reduction) and Bayesmethod; the false positive rate is 5. 19%, less than the othermethods.
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Key words:
- rough set /
- Bayes classifier /
- virus /
- network security
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