Multi-dimensional trust for context-aware services computing.

• We introduce a novel trust model of IoT services considering dynamic environments. • We define a subjective, objective, collusion resistant and bootstrapped trust model. • We use the log score and error estimation to assess the model's prediction accuracy. • We use the ROC curves to measure the accuracy of the bootstrapping trust mechanism. • We employ real dataset to show the performance and efficiency of the proposed model. The paper addresses the problem of trust management of cloud, fog and IoT services in dynamically changing environments. The continuous dynamic environment is one of the challenges that trustworthy services management in the cloud, fog and IoT settings faces. Services in such an environmental context have difficulty securing an acceptable quality of service (QoS). This article proposes a trust management framework that establishes service trust by considering the direct trust from the truster (subjective trust), aggregating referrals about the service in a collusion-resistant manner (objective trust), and bootstrapping new services. We introduce a subjective trust model based on the formalism of dependency networks to dynamically predict the provided QoS in response to context environment changes. The proposed approach leverages the dependency relations that exist among the QoS metrics and environmental context variables. The novelty at the subjective trust level lies in considering the dynamic cyclic dependency relations that enhances the prediction accuracy. However, subjective trust based on direct interactions could be insufficient to make the trust estimate credible. Hence, on top of the subjective layer, we propose an objective trust management model resilient to collusion attacks by leveraging the power of mass collaboration among referees. Finally, we propose a bootstrapping mechanism that is resilient to the white-washing attacks by observing the behaviours of newcomer services with no trust resources using the concept of social adoption to estimate their initial trust values. Experiments conducted on real-life and synthetic datasets demonstrate the effectiveness of our approach compared with state-of-the-art approaches. We used the statistical log score to assess the model's prediction accuracy and employed the estimation error of the objective trust as indicated by referees relative to the one calculated by the multi-round simulation. The ROC (Receiver Operating Characteristic) curves are finally used to measure the accuracy of the classifier used in the proposed trust bootstrapping mechanism in providing accurate initial trust values. The main findings of the paper are around the new trust model of cloud, fog and IoT services considering their dynamically changing environments. The first finding is that the prediction of the provided QoS shows better results when it is dynamic and responds to context environment changes by leveraging the dynamic dependency network linking the QoS metrics and context variables of the environment. The second finding is that the objective trust performs better when it is resilient to collusion attacks by leveraging the power of mass collaboration among referees. The third finding is that the bootstrapping mechanism that observes the behaviours of new comer services with no trust resources using the concept of social adoption to estimate their initial trust values excels by being resilient to white watching attacks. [ABSTRACT FROM AUTHOR]