摘要
网络行为被害性分析对于电信网络诈骗犯罪的防控具有深远意义。通过研究用户与网站交互产生的网络流量,提出一种基于网络流量分析的电信网络诈骗犯罪用户网络行为被害性识别模型,分析不同网络行为特征之间的关联规则,重构网络行为序列特征,同时结合随机森林算法评估网络行为的被害性。在被害人网络行为数据集基础上进行实验,证明模型能够有效提升网络行为被害性识别准确率。
The analysis of network victimization is of great significance to the prevention and control of telecom fraud.By studying the network traffic generated by the interaction between users and websites,a victimization identification model of telecom fraud crime based on network behavior flow analysis was proposed,the association rules between different behavior characteristics were analyzed,the behavior sequence features were reconstructed,and the victimization of network behavior sequence with random forest algorithm was evaluated.Based on the network behavior data set of public security organs,the experiment proves that the model can effectively improve the recognition accuracy of network behavior victimization.
作者
周胜利
徐啸炀
ZHOU Shengli;XU Xiaoyang(Zhejiang Police College,Hangzhou 310051,China)
出处
《电信科学》
2021年第2期125-134,共10页
Telecommunications Science
基金
浙江省公益技术研究计划(No.LGF20G030001)
校局合作项目(No.2020XJY011)
国家级创新项目(No.11483)。
关键词
网络流量
网络行为编码
关联规则挖掘
被害性分析
network traffic
network behavior coding
association rules mining
victimization analysis
