基于分类规则信息熵的报文处理算法

Packet Processing Algorithm Based on Classification Rule Information Entropy

针对分类规则的预处理问题,提出离群属性检测分类算法。在报文分类规则属性域上计算离群属性子集,利用规则属性加权矢量计算加权距离,分析规则加权邻域的子空间离群影响因子,通过与离群因子阈值比较生成频繁匹配子集对规则进行预处理。实验结果表明,该算法能缩小后续报文的匹配范围,提高报文转发的匹配精度与速度。

Aiming at the preprocessing problem for classification rule,this paper proposes outlier attribute detection classification algorithm.It accounts outlier attributes subspace on packet classification rule attribute,uses rule attribute weighted vector to calculate weighted distance,analyzes subspace outlier influence factor of rule weighted neighborhood area,and generates frequent matching subset by comparing with outlier factor threshold value.Experimental results show that this algorithm can shorten the matching rang of follow packet,enhance matching precision and speed of packet forwarding.