NSF Award: Learning Dynamic and Robust Defenses Against Co-Adaptive Spammers

Award number: NSF-1930941

Duration (expected): 3 years (10/01/2019 - 09/30/2022)

Award title: Learning Dynamic and Robust Defenses Against Co-Adaptive Spammers

Principal Investigator : Philip S. Yu (psyu@cs.uic.edu)

Students:
  • Yingtong Dou (ydou5@uic.edu)
  • Zhiwei Liu (zliu213@uic.edu)
  • Lichao Sun (lsun29@uic.edu)

    • Project Goals: 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.

    SafeGraph:We have a Github project named SafeGraph, which includes code, tools, and resources towards secure graph-based machine learning. We have released two graph-based fraud (including opinion spam) detection toolbox. The DGFraud is a graph neural network-based fraud detection toolbox, and the UGFraud is an unsupervised graph-based fraud detection toolbox. We also curate several weekly-updated paper lists related to graph adversarial learning and graph-based fraud detection

    Recent Publications:

  • Yingtong Dou, Zhiwei Liu, Li Sun, Yutong Deng, Hao Peng, Philip S. Yu. "Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters," ACM CIKM, 2020. [Paper] [Code&Data]
  • Yingtong Dou, Guixiang Ma, Philip S. Yu, Sihong Xie. "Robust Spammer Detection by Nash Reinforcement Learning," ACM KDD, 2020。 [Paper] [Code&Data] [Slides] [Video]
  • Zhiwei Liu, Yingtong Dou, Yutong Deng, Hao Peng, Philip S. Yu. "Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection," ACM SIGIR, 2020. [Paper] [Code&Data] [Slides]
  • Jiawei Zhang, Bowen Dong, Philip S. Yu. "Deep Diffusive Neural Network-based Fake News Detection from Heterogeneous Social Networks," IEEE Big Data, 2019. [Paper]

    • Related Publications:

  • Lichao Sun, Yingtong Dou, Carl Yang, Ji Wang, Philip S. Yu, Bo Li. "Adversarial Attack and Defense on Graph Data: A Survey," arXiv preprint. [Paper]
  • Vahid Noroozi, Sara Bahaadini, Samira Sheikhi, Nooshin Mojab, Philip S. Yu. "Leveraging Semi-Supervised Learning for Fairness using Neural Networks," IEEE ICMLA, 2019. [Paper]
  • Hui Yan, Siyu Liu, Philip S. Yu. "From Joint Feature Selection and Self-Representation Learning to Robust Multi-view Subspace Clustering," IEEE ICDM, 2019. [Paper]

    • Acknowledgement: This material is based upon work supported by the National Science Foundation under Grant No. (NSF-1930941).

    Disclaimer: Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.