COPLINK Center: Social Network Analysis and Identity Deception Detection for Law Enforcement and Homeland Security

为了阻止未来的任何恐怖袭击，执法和情报机构正在尽一切努力加强国家安全。在各级执法和情报机构中，开发和实施先进的信息收集、共享和分析技术成为当务之急。犯罪信息是所有类型犯罪数据的核心要素。然而，我们发现自己缺乏有效识别罪犯身份和及时识别罪犯之间关系的技术。这项研究的目的是通过开发一个全面的框架来应用数据挖掘、欺骗检测和社会网络分析来帮助识别罪犯的身份和罪犯之间的关系，从而解决这些罪犯身份管理方面的挑战。将与真实用户和执法组织一起评估这些技术；还将审查相关的技术采用和政策问题。这项研究的主要智力价值有四个方面：(A)开发适用于执法应用但也可以应用于其他具有类似特征的领域的新的数据挖掘和知识发现模型和技术；(B)为犯罪身份欺骗的形成和表现建立理论基础；(C)开发犯罪网络自动生成和分析的框架；以及(D)研究在现实世界中对所开发技术的接受和采用。这项研究的更广泛影响包括：减少执法应用程序中的认知和信息过载；提高情报和执法机构发现、预防和应对犯罪和恐怖主义事件的能力；以及提供促进执法和情报机构之间信息共享和合作的技术。与执法机构的几个伙伴关系将确保产生影响。

To deter any future terrorist attacks, law enforcement and intelligence agencies are making every effort to enhance national security. It becomes the top priority to develop and implement advanced technologies for information gathering, sharing, and analysis in all levels of law enforcement and intelligence agencies. Criminal information is the central element in all types of crime data. However, we find ourselves lacking techniques to both identifying a criminal's identity effectively and to recognizing relationships among criminals promptly. The motivation of this research is to address these criminal identity management challenges by developing a comprehensive framework for applying data mining, deception detection, and social network analysis to help identify criminal identities and relationships among criminals. These technologies will be evaluated with real users and law enforcement organizations; related technology adoption and policy issues will also be examined. The primary intellectual merit of this research is four-fold: (a) developing new data mining and knowledge discovery models and techniques that are appropriate for law enforcement applications but can also be applied in other domains demonstrating similar characteristics, (b) developing a theoretic basis for the formation and the representation of criminal identity deception, (c) developing a framework for automated criminal network generation and analysis, and (d) studying the acceptance and adoption of the developed technologies in real-world settings. The broader impacts of this research include: reducing cognitive and information overload in law enforcement applications; improving intelligence and law enforcement agencies' abilities to detect, prevent, and respond to crimes and terrorism events; and providing techniques that foster information sharing and collaboration among law enforcement and intelligence agencies. Several partnerships with law enforcement agencies will ensure impact.