基于模糊决策树的保险企业数据异常访问检测方法

Abnormal Accessing Diagnosis Method of Insurance Data Based on Fuzzy Decision Tree

保险数据的安全已经成为影响企业效益和声誉的重大问题。为了有效检测日常交易数据中的异常访问行为,提出了一种基于模糊决策树的检测方法,该方法将能够把决策树的归纳学习能力和模糊集合所具有的表达能力相结合,可以有效地发现交易记录中的异常访问行为。通过实验分析,能够提高检测的准确性和效率。

Insurance data security has become the major issues which affect the enterprisese performance and reputation of a company.In order to effectively detect abnormal data access behavior of daily trading,the paper proposed a abnormal access diagnosis algorithm based on fuzzy decision tree.the method will be able to put the decision tree inductive learning ability and fuzzy collection has the ability to express the combination,can effectively found abnormal access behavior in the transaction log.It can enhance the accuracy and efficiency of shearer fault by analyzing the experiment.