Abstract
Abstract Indirect trust computation based on recommendations form an important component in trust-based access control models for pervasive environment. It can provide the service provider the confidence to interact with unknown service requesters. However, recommendation-based indirect trust computation is vulnerable to various types of attacks. This paper proposes a defense mechanism for filtering out dishonest recommendations based on a measure of dissimilarity function between the two subsets. A subset of recommendations with the highest measure of dissimilarity is considered as a set of dishonest recommendations. To analyze the effectiveness of the proposed approach, we have simulated three inherent attack scenarios for recommendation models (bad mouthing, ballot stuffing, and random opinion attack). The simulation results show that the proposed approach can effectively filter out the dishonest recommendations based on the majority rule. A comparison between the exiting schemes and our proposed approach is also given.
Highlights
The rapid development of collaborative, dynamic, and open environments has increased awareness on security issues
Since median is resistent to outlier, we have proposed a dissimilarity function that captures how dissimilar a recommendation class is from the median of the recommendation set
Experimental evaluation shows the effectiveness of our proposed method in filtering dishonest recommendations in comparison with the base model
Summary
The rapid development of collaborative, dynamic, and open environments has increased awareness on security issues. Related work The dynamism of pervasive computing environment allows ad hoc interaction of known and unknown autonomous entities that are unfamiliar and possibly hostile In such environment where the service providers have no personal experience with unknown service requesters, trust and recommendation models are used to evaluate the trustworthiness of unfamiliar entities. A context-specific and reputationbased trust model for pervasive computing environment was proposed [25] to detect malicious recommendation based on control chart method. Deno et al [26] proposed an iterative filtering method for the process of detecting malicious recommendations In this model [26], an average trust value (Tavg) of all the recommendations received (TR) is calculated. After detecting the set Rdomaindishonest, we remove all recommendations that fall under the dishonest recommendation classes
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