Identifying Collusion Attack Based on Preference Similarity in Mixed Reputation Recommendation Model

摘要

Due to the emergence of a large number of new nodes in peer-to-peer network, the trust matrix is sparse and data is insufficient. Therefore, global trusts of peers are inaccurate which are computed by trust matrix iteration and the success rates of transactions become low. PSRTrust, a mixed trust model combining global trusts and local trusts based on preference similarities, is proposed to restore sparse trust matrix by Similarity Random Walk. It optimizes the unreasonable assumption, improves the power-law distribution, and identifies collusion attack by preference similarity. Mathematic analyses and simulation results show that the proposed model is more robust under general conditions that collusion attacks in an attempt to deliberately subvert the system, and the success rates of transactions are higher compared to the current trust model.