Identification for Strategically Malicious Participants

Trust metric as an effective manner to deal with diverse attacks has been successfully applied in realistic interactional networked systems, such as eBay, Amazon, etc. Nevertheless, the existence of strategically malicious behaviors, such as colluding and disguise attacks, prevents most existing trust metrics from inferring rational trust taking into account they markedly promotes the trust of misbehaved participants through collaborating with each other, hence relying only on the trust to conduct interactional behaviors among strange participants becomes inappropriate. In this paper, we propose a misbehavior identification mechanism TrustId through clustering different categories of participants into appropriate communities. The primary contributions include: (i) we propose an information entropy-controlled cluster algorithm with respect to diverse sophisticated interactional participants; (ii) we extract three facets of attributes to derive clustering for trust-enabled interactional networks; (iii) we conduct extensive experiments to evaluate the efficiency of our proposed misbehavior identification mechanism, and the results exhibit our TrustId correctly identifies strategically misbehaved participants and significantly outperforms EigenTrust and PathTrust against the representative colluding and disguise attacks.