A Trust-Based Detecting Mechanism against Profile Injection Attacks in Recommender Systems

Recommender systems could be applied in grid environment to help grid users select more suitable services by making high quality personalized recommendations. Also, recommendation could be employed in the virtual machines managing platform to measure the performance and creditability of each virtual machine. However, such systems have been shown to be vulnerable to profile injection attacks (shilling attacks), attacks that involve the insertion of malicious profiles into the ratings database for the purpose of altering the system´s recommendation behavior. In this paper we introduce and evaluate a new trust-based detecting algorithm for protecting recommender systems against profile injection attacks. Moreover, we discuss the combination of our trust-based metrics with previous metrics such as RDMA in profile-level and item-level respectively. In the end, we show these metrics can lead to improved detecting accuracy experimentally.