Simulation Based Evaluation and Tuning of Distributed Fraud Detection Algorithm

Abstract

A community based fraud detection is one of the methods to ensure trustworthiness of Internet resources. The TrustSearch platform has been developed to provide community based fraud detection services. It allows Internet user to submit application reporting potential fraudulent Internet resources and relies on a consensus seeking algorithm to approve or reject the application. The system exhibits complex and dynamic behavior, and simulation is used to evaluate its performance and to determine appropriate operational parameters. The objective is to find an appropriate trade-off between evaluation accuracy and efficiency what is a characteristic challenge in distributed decision-making systems. An agent-oriented simulation model is developed and experimental studies are conducted. It has been shown that sufficiently high evaluation accuracy can be achieved and the results are remarkably robust. However, a relatively large number of participants is required. The community based platform uses blockchain technologies to reward participants for their contributions.