Search rank fraud, or posting large numbers of paid activities for products hosted in online services, intends to give the illusion of grassroots engagement and to boost financial gains, promote malware and even assist censorship efforts. Search rank fraud continues to be a significant problem after years of investment by service providers and the academic community. This lack of progress is partly due to an incomplete and inaccurate understanding of the diverse community of professional raters. This project builds on the thesis that to be effective, fraud detection and prevention efforts need to involve the organizations and individuals who contribute to search rank fraud. To this end, this project is designing and developing techniques to validate professional raters and to study their capabilities, behaviors and detection avoidance strategies. The project introduces a new generation of solutions to address and neutralize the effects of search rank fraud. This project will help protect millions of online service users from misleading information, substandard products and even malware and censorship-enabling apps.
This project introduces a novel approach to study, detect and prevent fraud in online services, and to evaluate developed solutions. The team is building a platform, techniques, and protocols to collect data about the online and mobile device behaviors of professional raters and honest users of peer-opinion services, and to evaluate developed solutions using live raters. Also, the team is designing fraud generators to produce benchmark datasets of synthetic but realistic timelines of fraudulent activities. The team is using an adversarial learning approach to iteratively train neural networks to disentangle different types of fraud from honest behaviors. Finally, the team will leverage unique insights extracted from the collected data, along with network representation learning and graph partitioning techniques, to develop predictors that attribute fraud and identify user accounts controlled by the same organization.
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.
