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SOCIALCOM
2010

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

9 years 7 months ago
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.
Yuhong Liu, Yan (Lindsay) Sun
Added 15 Feb 2011
Updated 15 Feb 2011
Type Journal
Year 2010
Where SOCIALCOM
Authors Yuhong Liu, Yan (Lindsay) Sun
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