面向新型跨境网络有组织犯罪的开源情报获取与利用方法

Open Source Intelligence Acquisition and Utilization Methods for New Types of Cross-border Organized Cybercrime

随着有组织犯罪网络化、跨境化特征的凸显,利用开源情报进行基于知识图谱的网络犯罪大数据关联分析和基于社会网络分析的犯罪人员识别,能够有效打击犯罪行为以及最大限度压缩犯罪分子的生存空间。以“跨境电信诈骗犯罪治理”为例,利用开源数据构建跨境电信诈骗的事件知识图谱以及各事件的利益相关者网络,识别出犯罪集团的核心人物以及潜在的犯罪分子。网络犯罪大多数以犯罪团伙或集团的形式作案,为躲避监管和打击,犯罪分子将犯罪窝点、通讯工具等逐步向境外转移,并且有目的性地针对我国公民实施跨境网络犯罪。将案件、事件等图谱化,构建大规模知识节点和关系链接,能够有效发现隐藏的人员、资金、技术、信息流等,有助于公安部门采取精准打击手段遏制犯罪行为。

With the emergence of the network and cross-border characteristics of organized crime,the use of open source intelligence to conduct cybercrime big data association analysis based on knowledge graphs and criminal identification based on social network analysis can effectively combat criminal acts and minimize the living space of criminals.Taking"Cross-border Telecom Fraud Criminal Governance"as an example,use open-source data to construct a knowledge graph of cross-border telecom fraud events and a network of stakeholders in each event to identify the core figures of criminal groups and potential criminals.Most cybercrimes commit crimes in the form of criminal gangs or groups.In order to avoid supervision and combat,criminals gradually transfer criminal dens and communication tools abroad,and purposefully implement cross-border networks against Chinese citizens crime.Graphing cases,incidents,etc.,and building large-scale knowledge nodes and relationship links,can effectively discover hidden personnel,funds,technology,information flows,etc.,and help the public security department to adopt precise combat methods to curb criminal behavior.