EAGER: Collaborative: A Multi-Disciplinary Framework for Modeling Spatial, Temporal and Social Dynamics of Cyber Criminals

该项目设计并部署了一个多学科框架来模拟网络罪犯的空间、时间和社会动态。该框架融合了计算机科学和犯罪学的理论。具体而言，项目目标是a)应用和验证一般犯罪学领域的现有理论(特别是Akers？社会学习理论和Gottfredson和Hirschi？（犯罪通论）研究网络犯罪；b)获得新颖的互联网使用特征，作为网络犯罪的指纹；c)设计分类算法（基于多重分形分析和petri-net设计），通过整合上述两个目标的理论和实践成果，随后对网络犯罪分子的多种动态建模；d)广泛测试和验证项目成果。这个项目的核心新颖之处在于使用真实的互联网数据（最初是一个精通网络的大学样本），这些数据是连续收集的，不引人注目的，同时仍然保持高度的隐私。该项目的成果将产生深远的影响。它为融合社会科学（特别是犯罪学）和网络安全的专业知识奠定了基础，因此，一般犯罪学的现有理论可以在研究网络犯罪方面进行实证检验。识别与网络犯罪相关的唯一互联网指纹，将为了解网络犯罪中以人为中心的方面提供新的见解，这是目前所缺乏的。设计的分类算法将为网络防御者提供新的工具，从预防、检测、法医调查和起诉等多个角度打击网络犯罪。

This project designs and deploys a multi-disciplinary framework to model spatial, temporal and social dynamics of cyber criminals. The framework fuses theories in both computer science and criminology. Specifically, project objectives are a) Apply and validate existing theories in the realm of general criminology (in particular Akers? social learning theory and Gottfredson and Hirschi?s general theory of crime) to study cyber crimes; b) Derive novel Internet usage features as fingerprints for cyber crimes; c) Design classification algorithms (based on multi-fractal analysis and petri-net designs) to subsequently model multiple dynamics of cyber criminals by integrating theoretical and practical outcomes from the above two objectives; and d) Extensively test and validate project outcomes. The core novelty of this project is in using real Internet data from subjects (initially a cyber savvy college sample) that is collected continuously, unobtrusively, while still preserving a high degree of privacy. Outcomes of this project will have far reaching impacts. It lays a foundation for fusing expertise in social sciences (specifically criminology) and cyber security, as a result of which existing theories in general criminology can be empirically tested for practical validity in studying cyber crimes. The identification of unique Internet fingerprints associating with cyber crimes will provide new insights into human centered aspects of cyber crimes, which is lacking today. The classification algorithms designed will provide cyber defenders with new tools to combat cyber crimes from multiple perspectives including prevention, detection, forensic investigations and prosecution.