基于个性特征仿真邮件分析系统挖掘犯罪网络核心

Mining Key Members of Crime Networks Based on Personality Trait Simulation Email Analysis System

数据挖掘应用于犯罪集团或恐怖组织社会网络分析是一种新兴的研究方法,国内外在分析犯罪和恐怖组织之间通信行为方面的研究工作亟待深入.为了模拟社会网络中个体利用电子邮件进行通信的规律,设计了一种基于个性特征的仿真邮件分析系统MEP,提出一种利用个性特征判别矩阵计算个性特征矢量各个维度权重的新方法,借助符合用户个性特征的正态分布模型模拟真实的邮件通信行为.为了挖掘犯罪网络的核心成员,提出了一种基于社会网络分析挖掘犯罪组织核心成员的算法CNKM(Crime Network Key Membermining),并利用时间序列分析方法对邮件的收发规律进行深入分析,发现异常通信事件.实验证明了该文提出的仿真邮件分析系统的有效性和实用性,模拟邮件通信的平均误差小于10%,并验证了CNKM算法的有效性.

It is a new paradigm to apply data mining technologies to analyze the crime groups and terrorist social networks, there is little work being done on analyzing the communication behavior of criminal and terrorist groups. This paper designed a simulation email system based on personality trait dimensions, called MEP, to model the email users＇ traffic behavior, proposed a new approach of computing the weight of each dimension in a personality trait vector by using personality trait judge matrix, and simulated the real-world email communication behavior based on normal distribution model satisfying users＇ personality trait. This paper proposed a social network analysis based algorithm called CNKM （Crime Network Key Member mining） to mine key members of a crime group, and employed time-series analysis techniques to discover the email sending and receiving rules in order to detect the abnormal communication cases. The experimental results show the efficiency and usability of the simulation email analysis system, the average simulation error is less than 10%, and demonstrate that CNKM is efficient.