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

社交网络的影响范围使其成为恶意用户发布垃圾信息的诱人目标，这些信息旨在污染社会环境，欺骗正常用户，或影响政治观点。越来越多的恶意内容造成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建模来减轻恶意在线行为。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，成为分析和减轻网络不当行为的强大工具。通过对在线概念、内容和参与者之间的关系进行建模，该图将支持对恶意社区演变和恶意行为检测的研究；图本身将被设计为可适应安全上下文，而底层方法将被设计为可用于需要在线建模交互的其他应用程序。为此，该团队将与研究界共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整合到教学材料中，以便广泛传播。该团队还将通过暑期系列教程和研究活动吸引本科生，包括那些来自计算机领域代表性不足的群体的学生。该项目侧重于开发一个整体框架来构建动态恶意知识图谱，围绕三个主要目标组织。第一个重点是开发一种实时恶意内容检测器，以解决诸如特征变化、实时可扩展处理和标签稀缺性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，目标是在增加图的大小和结构的同时识别复杂的、隐蔽的恶意行为者。第三个重点是知识图谱本身的设计，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真实世界收集的数据集上进行评估，然后将其部署到真实世界的社交网络中进行全面评估。该奖项反映了美国国家科学基金会的法定使命，并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，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.