Using Kl Divergence for Credibility Assessment

In reputation systems, agents collectively estimate the others' behaviours through feedbacks to decide with whom they can interact. To avoid manipulations, most reputation systems weight feedbacks with respect to the agents' reputation. However, these systems are sensitive to some strategic manipulations, like oscillating attacks or whitewashing. In this paper, we propose (1) a credibility measure of feedbacks based on the Kullback-Leibler divergence to detect malicious behaviours and (2) filtering functions to enhance already known reputation functions.