Dynamically Adaptive Recommender Filtering Scheme to Defend against Dishonest Recommenders in a MANET

Trust management frameworks play a very important role in securing the mobile ad hoc networks against various insider attacks that could occur during data forwarding. The success of a trust management framework greatly depends upon the proper design of each of its major components including the direct trust computation component as well as the indirect trust computation component. Specifically, the indirect trust computation component should be robust to handle the dishonest recommendations. In this paper, we propose a novel and effective scheme used to design a robust indirect trust computation component called as RecommFilter which can overcome the various attacks caused by dishonest recommendations. Four components namely, Recommendation Selection module, Recommendation Filtering module, Recommendation Evaluation module and Recommendation Trust Update module work in close collaboration to filter out the dishonest recommendations and protect against slandering attacks, bad-mouthing attacks as well as collusive attacks. The novelty of the proposed scheme is that it employs a combination of personal experience based approach as well as majority rule based approach wherein the Selection Module using the personal experiences involves a multi-dimensional trust represented using the Dempster Shafer Theory of evidences and the filtering module using the majority rule involves a clustering based approach performed through an opinion similarity measure computed using the Jousselmes distance between two basic probability assignments (bpa). Experimental results show that the proposed scheme is robust to different dishonest recommendation attacks and accurate in the detection of dishonest recommenders.