Friends or Foes: Detecting Dishonest Recommenders in Online Social Networks

Viral marketing is becoming important due to the popularity of online social networks (OSNs) and the fact that many users have integrated OSNs into their daily activities, e.g., they provide recommendations to their friends on the products they purchased, or they make decision based on received recommendations. Nevertheless, this also opens door for "shill attack": dishonest users may give wrong recommendations so as to distort the normal sales distribution. In this paper, we propose a detection mechanism to discover these dishonest users in OSNs. In particular, we present two fully distributed algorithms to detect attackers in both (1) the baseline shill attack and (2) the intelligent shill attack. We quantify the performance of our algorithms by deriving the probability of false positive, probability of false negative and distribution function of time needed to detect these dishonest users. Extensive simulations are carried to illustrate the impact of shill attack and the effectiveness of our detection algorithms. The methodology we present here will enhance the security level of viral marketing in OSNs.