SaTC: CORE: Small: Collaborative: Learning Dynamic and Robust Defenses Against Co-Adaptive Spammers

SaTC：核心：小型：协作：学习针对自适应垃圾邮件发送者的动态且强大的防御

• 批准号: 1931042
• 负责人: Sihong Xie
• 金额: $ 25万
• 依托单位: Lehigh University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931042&HistoricalAwards=false
• 关键词: SaTC; CORE; Small; Collaborative; Learning

Online reputation systems are ubiquitous for customers to evaluate businesses, products, people, and organizations based on reviews from the crowd. For example, Yelp and TripAdvisor rank restaurants and hotels based on user reviews, and RateMDs allows patients to review doctors and hospitals. These systems can however be leveraged by spammers to mislead and manipulate the inexperienced customers with fake but well-disguised reviews (spams). To comprehensively protect customers and honest businesses, advanced spam detection techniques have been deployed. Nonetheless, intelligent spammers can still probe and then evolve to bypass the deployed detectors. This project investigates dynamic and robust countermeasures to defeat the evolving spammers. This research will allow regulatory agencies to enforce a more fair, transparent, and trustworthy online environment, encourage business owners to offer higher quality products and services rather than fake opinions, and ultimately, allow consumers to increasingly rely on the reputation systems confidently to save money, time and even lives.The project will investigate the design of adaptive spam detection technologies and systems against intelligent spammers that learn to bypass static detectors. The investigation will follow two principles: (1) the goals and workings of the detectors and spammers can be sensed through their behaviors; (2) both parties should act dynamically to optimally defeat their opponents who co-adapt with the other's behaviors. Based on these principles, the researchers aim to: (i) investigate the footprint of dynamic spamming and formalize the gained insights into evasion models against static detectors; (ii) model the interactions between the evolving spammer and dynamic detections through deep reinforcement learning and Markov games; and (iii) introduce multiple cooperative spammers to inform more complex spammer-detector co-adaptations through multi-agent and hierarchical reinforcement learning. The research aims will be complemented by metrics and evaluations that capture realistic spammer and detector goals and constraints. The project will result in datasets, algorithms, and testbed system for the research community, and gamified educational software and materials to increase awareness of fake contents among a broader population.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.

在线信誉系统无处不在，客户可以根据人群的评论来评估企业，产品，人员和组织。例如，Yelp和TripAdvisor根据用户评论对餐馆和酒店进行排名，RateMD允许患者评论医生和医院。然而，垃圾邮件发送者可以利用这些系统来误导和操纵没有经验的客户，使用虚假但伪装得很好的评论（垃圾邮件）。为了全面保护客户和诚实的企业，我们部署了先进的垃圾邮件检测技术。尽管如此，智能垃圾邮件发送者仍然可以探测，然后进化到绕过部署的探测器。这个项目研究动态和强大的对策，以击败不断发展的垃圾邮件发送者。这项研究将使监管机构能够实施一个更加公平，透明和值得信赖的在线环境，鼓励企业主提供更高质量的产品和服务，而不是虚假的意见，并最终使消费者能够越来越自信地依赖声誉系统来省钱，该项目将研究自适应垃圾邮件检测技术和系统的设计，以对抗智能垃圾邮件发送者，绕过静电探测器。调查将遵循两个原则：（1）检测器和垃圾邮件发送者的目标和工作可以通过他们的行为来感知;（2）双方都应该动态地采取行动，以最佳方式击败与对方行为相适应的对手。基于这些原则，研究人员的目标是：（i）调查动态垃圾邮件的足迹，并将所获得的对静态检测器的规避模型的见解形式化;（ii）通过深度强化学习和马尔可夫博弈对不断发展的垃圾邮件发送者和动态检测之间的相互作用进行建模;以及（iii）引入多个合作的垃圾邮件发送者，以通过多代理和分层强化学习来通知更复杂的垃圾邮件发送者-检测器协同适应。研究目标将得到补充的指标和评估，捕捉现实的垃圾邮件发送者和检测器的目标和限制。该项目将为研究界提供数据集、算法和测试平台系统，以及游戏化的教育软件和材料，以提高更广泛人群对虚假内容的认识。该奖项反映了NSF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。

项目成果

Self-learn to Explain Siamese Networks Robustly

• DOI: 10.1109/icdm51629.2021.00116
• 发表时间: 2021-09
• 期刊: 2021 IEEE International Conference on Data Mining (ICDM)
• 作者: Chao Chen;Yifan Shen;Guixiang Ma;Xiangnan Kong;S. Rangarajan;Xi Zhang;Sihong Xie

Inconsistent Matters: A Knowledge-Guided Dual-Consistency Network for Multi-Modal Rumor Detection

• DOI: 10.1109/tkde.2023.3275586
• 发表时间: 2023-06
• 期刊: IEEE Transactions on Knowledge and Data Engineering
• 影响因子: 8.9
• 作者: Mengzhu Sun;Xi Zhang;Jianqiang Ma;Yazheng Liu

Interpretable and Effective Reinforcement Learning for Attacking against Graph-based Rumor Detection

• DOI: 10.1109/ijcnn54540.2023.10191290
• 发表时间: 2022-01
• 期刊: 2023 International Joint Conference on Neural Networks (IJCNN)
• 作者: Yuefei Lyu;Xiaoyu Yang;Jiaxin Liu;Sihong Xie;Xi Zhang

Certification and Trade-off of Multiple Fairness Criteria in Graph-based Spam Detection

• DOI: 10.1145/3459637.3482325
• 发表时间: 2021-10
• 期刊: Proceedings of the 30th ACM International Conference on Information & Knowledge Management
• 作者: Kai Burkholder;Kenny Kwock;Yuesheng Xu;Jiaxin Liu;Chao Chen;Sihong Xie

Active Search using Meta-Bandits

• DOI: 10.1145/3340531.3417409
• 发表时间: 2020-10
• 期刊: Proceedings of the 29th ACM International Conference on Information & Knowledge Management
• 作者: Shengli Zhu;Jakob Coles;Sihong Xie