A multi-criteria detection scheme of collusive fraud organization for reputation aggregation in social networks

In social networks, reputation aggregation is an effective approach for recognizing malicious behaviors and individuals. However, organized collusive fraud to obtain a high reputation is the most common and most harmful type of widespread network attack. Therefore, countering the collusion in reputation aggregation systems and further detecting the collusive fraud organizations (CFO) is a significant challenge. In this study, we propose a multi-criteria detection scheme of collusive fraud organization, named MD-CFO, to identify CFO in social networks. This scheme is based on a new universal reputation aggregation method, which includes the calculation of a reputation score and a universal factor. Moreover, five detection criteria and their corresponding factors, i.e., the rating difference fraud factor, rating frequency fraud factor, collaborative behavior fraud factor, suspicious relationship fraud factor, and CFO factor, are used to evaluate the likelihood of being a colluder. Furthermore, we propose three algorithms for detecting CFO and colluders. To prevent false-positive detection results, a correction mechanism called time slice verification (TSV) is used to certify a node's likelihood of suspicion or fraud in a series of time slices, thereby excluding honest nodes from CFO detection. Finally, empirical simulations are used to test the feasibility and effectiveness of our scheme.