基于改进变精度粗糙集的漏洞威胁评估

变精度粗糙集理论能有效处理带噪声的数据，但其移植性较弱。针对这种情况，引入阈值参数α，提出了一种改进的变精度粗糙集漏洞威胁评估模型。首先，根据漏洞特征属性建立评估决策表；然后，使用k均值算法对连续属性进行离散化处理；接下来，通过多次计算，调整参数β和α的值，进行属性约简并提取概率决策规则，构造决策规则库；最后，将测试数据与规则库进行匹配，得到漏洞威胁评估结果。仿真实验表明，所提方法的评估正确率比改进前提高了19.66个百分点，并且移植性有所增强。

Vulnerability threat assessment based on improved variable precision rough set

Variable Precision Rough Set (VPRS) can effectively process the noise data, but its portability is not good. Aiming at this problem, an improved vulnerability threat assessment model was proposed by introducing the threshold parameter α. First of all, an assessment decision table was created according to characteristic properties of vulnerability. Then, k-means algorithm was used to discretize the continuous attributes. Next, by adjusting the value of β and α, the attributes were reducted and the probabilistic decision rules were concluded. Finally, the test data was matched with the rule base and the vulnerability assessment results were obtained. The simulation results show that the accuracy of the proposed method is 19.66 percentage points higher than that of VPRS method, and the transplantability is enhanced.