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

社交网络的覆盖范围使其成为恶意用户发布垃圾邮件的诱人目标，这些垃圾邮件旨在污染社会环境、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，以减轻恶意在线行为。受人类学习过程的启发，该图表将逐渐积累知识，随着时间的推移，它将成为分析和缓解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演变和恶意行为检测的研究；该图本身将被设计为可跨安全上下文和底层方法设计为可用于其他需要在线建模交互的应用程序。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，并将以书籍章节、课程材料和教程的形式将研究结果纳入教学材料，以便广泛传播。该团队还将通过暑期辅导系列和研究活动，吸引本科生，包括那些来自计算机行业代表不足的群体的学生。该项目专注于开发一个整体框架来构建动态恶意知识图，围绕三个主要推动力进行组织。第一个推力是开发一个实时恶意内容检测器，以解决诸如功能变化、实时可扩展处理和标签稀缺等具有挑战性的问题。第二个重点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，目标是识别复杂的、隐蔽的恶意行为者，同时扩大图表的大小和结构。第三个重点是知识图本身的设计，包括它的结构、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收集的真实世界数据集上进行评估，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，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.