Information theoretic-based detection and removal of slander and/or false-praise attacks for robust trustmanagement with Dempster-Shafer combination of linguistic fuzzy terms

摘要

Critical systems are progressively abandoning the traditional isolated and closed architectures,rnand adoptingmore federated solutions, in order to deal with orchestrated decisionmakingwithinrnlarge-scale infrastructures. Such an increasing connectivity and the possibility of dynamicallyrnintegrate constituents in a seamless manner by means of a decoupling middleware solution arerncausing the flouring of novel and previously unseen security threats, such as internal attacks conductedrnby camouflaged and/or compromised federated systems. Trust management is the mostrnefficient way for dealing with such attacks, so that each constituent computes a trust degree ofrnthe other interacting ones based on the direct experiences and of collected reputation scores. Anrnadversarymay negatively affect the overall process with false reputations,which must not be consideredrnwhen estimating a trust degree. Our work combines amulti-criteria linguistic fuzzy termrnformulation of the trust degree with the concept of entropy for measuring the divergence of certainrnscores from the other ones and to avoid to consider them during reputation aggregation. Arnset of experiments have been conducted in order to measure the quality and effectiveness of thernpresented approach.