CAREER: Holistic Framework for Constructing Dynamic Malicious Knowledge Bases in Social Networks

社交网络的影响范围使它们成为恶意用户发布垃圾消息的诱人目标，这些垃圾消息旨在污染社交环境，欺骗普通用户或影响政治观点。越来越多的恶意内容导致了越来越大的经济损失和不良的社会影响。该项目旨在通过动态恶意知识图对恶意在线行为进行建模，从而减轻恶意在线行为。受人类学习过程的启发，该图将逐渐积累知识，随着时间的推移，成为分析和减轻在线不当行为的强大工具。通过在线建模概念，内容和参与者之间的关系，该图将支持对恶意社区的演变和恶意行为检测的研究;该图本身将被设计为跨安全上下文的适应性，并且底层方法被设计为可用于需要在线建模交互的其他应用程序。为此，该小组将与研究界分享在该项目中开发的数据集和软件工具包，研究结果将以书籍章节、课程材料和教程的形式纳入教学材料，并广泛传播。该团队还将通过暑期教程系列和研究活动吸引本科生，包括来自计算领域代表性不足的群体的学生。该项目的重点是开发一个整体框架来构建动态恶意知识图，围绕三个主要目标进行组织。第一个重点是开发一个实时恶意内容检测器，解决具有挑战性的问题，如功能变化，实时可扩展处理和标签稀缺性。第二个目标是对积累的数据进行定期分析，以识别新出现的恶意模式、行为和潜在特征，目标是在增加图的大小和结构的同时识别复杂、隐蔽的恶意行为者。第三个重点是知识图本身的设计，包括其结构、构造、标签辅助进化和持续的自我监控。最终的解决方案将首先在真实世界的数据集上进行评估，然后部署到真实世界的社交网络中进行全面评估。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

Social networks’ reach makes them a tempting target for malicious users to post spam messages intended to pollute social environments, deceive normal users, or sway political opinions. The increasing amount of malicious content has resulted in growing economic loss and adverse social impacts. This project aims to mitigate malicious online behavior by modeling it through a dynamic malicious knowledge graph. Inspired by human learning processes, the graph will gradually accumulate knowledge, becoming over time a powerful tool for analyzing and mitigating online misbehavior. Through modeling the relationships between concepts, content, and actors online, the graph will support research on the evolution of malicious communities and the detection of malicious behavior; the graph itself will be designed to be adaptable across security contexts, and the underlying methods designed to be usable in other applications that require modeling interaction online. To this end, the team will share datasets and software toolkits developed in this project with the research community, and the findings will be integrated into instructional materials in the form of book chapters, course materials, and tutorials to be widely disseminated. The team will also engage undergraduate students, including those from under-represented groups in computing, through a summer tutorial series and research activities. This project focuses on developing a holistic framework to construct the dynamic malicious knowledge graph, organized around three main thrusts. The first thrust is to develop a real-time malicious content detector that addresses challenging issues such as feature variations, real-time scalable processing, and label scarcity. The second thrust is to conduct periodical analysis on the accumulated data to identify emerging malicious patterns, behaviors, and latent features with the goal of identifying sophisticated, stealthy malicious actors while growing the graph’s size and structure. The third thrust focuses on the design of the knowledge graph itself, including its structure, construction, label-aided evolution, and continuous self-monitoring. The resulting solutions will be first evaluated on real-world datasets to be gathered and then deployed into real-world social networks for full evaluation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.