Anomaly Detection in Feedback-based Reputation Systems through Temporal and Correlation Analysis

As the value of reputation systems is widely recognized, the incentive to manipulate such systems is rapidly growing. We propose TAUCA, a scheme that identifies malicious users and recovers reputation scores from a novel angle: combination of temporal analysis and user correlation analysis. Benefiting from the rich information in the time-domain, TAUCA identifies the products under attack, the time when attacks occur, and malicious users who insert dishonest ratings. TAUCA and two other representative schemes are tested against real user attack data collected through a cyber competition. TAUCA demonstrates significant advantages. It largely improves the detection rate and reduces the false alarm rate in the detection of malicious users. It also effectively reduces the bias in the recovered reputation scores.