Indirect Trust Computation Research Articles

A dynamic trust model in internet of things

Indicating trust or distrust of a node is a key issue in the trust management of IoT. However, there are some challenges for the management with anonymous nodes, inaccurate communication, etc. Due to huge number of nodes in IoT, one of the possible solutions is predicting trust and distrust values. In this paper, we propose a dynamic trust model based on direct and indirect trust computation, and the most important part of a model, trust prediction. The prediction method mainly depends on the combination of exponential smoothing and a Markov chain. We employ exponential smoothing to predict trustiness and a Markov chain to fix the deviation of the prediction. To test our scheme, we create a simulation to evaluate its performance and effectiveness. The simulation results are encouraging.

A Thorough Trust and Reputation Based RBAC Model for Secure Data Storage in the Cloud

Cloud computing is a widespread technology, which has attracted much attention nowadays. Among the many criteria that must be considered for data storage in the cloud, access control plays a vital role. Role-based access control (RBAC) is a well-known technique for secure data storage in the cloud. Since the traditional RBAC models are improper for open and decentralized environments, recently, some works have integrated the trust concept into the RBAC model. Nevertheless, they have not fully addressed the required security metrics of a trust-based system. Therefore, in this paper, we first introduce the security goals that should be considered in an efficient trust-based system. Second, we propose a novel trust and reputation based RBAC model that not only can properly withstand the security threats of trust-based RBAC models, but also is scalable as it has reasonable execution time. Third, we evaluate the proposed model using the famous trust network of advogato dataset. Eventually, we compare the proposed model with recently-published ones in terms of mean absolute error, execution time of indirect trust computation, and provided features. The achieved results are indicative of the priority of the proposed model to be employed in real cloud environments.

A Credible Bayesian-Based Trust Management Scheme for Wireless Sensor Networks

With the rapid development of wireless sensor networks (WSNs), the security of WSNs is an important issue in this field because of their vulnerabilities to attacks. Designing a reasonable trust management scheme that can evaluate the trust relationships among sensor nodes accurately is a challenging but meaningful task. In this paper, a credible Bayesian-based trust management scheme (BTMS) is proposed. The overall trust value is aggregated by both direct and indirect trust information. The former is calculated by a modified Bayesian equation and updated by a sliding window. The latter is computed by recommendations from a third party. Moreover, the indirect trust computation is invoked conditionally according to the uncertainty of direct trust calculated via Entropy Theory and malicious feedbacks are excluded. Meanwhile, different recommendations are appropriately weighted in light of the trust levels of recommenders. Simulations are conducted and the results show that, compared with existing approaches, the proposed trust model performs better in defeating attacks.

Design of a Novel Trust Model and its Application in Trust based Routing to Defend against Dishonest Recommenders

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. The current paper shows the application of a trust model involving a robust indirect trust computation component called as RecommFilter which has been proposed in our earlier work. It can overcome the various attacks caused by dishonest recommenders. The application involves the integration of the trust model with a routing protocol based upon a reliability measure called as Path Allegiance metric (PAM) which is a cumulative value obtained through the trust values of the on-path nodes upon each other. Experimental results show that the proposed scheme along with PAM routing protocol is robust to different dishonest recommendation attacks and accurate in the detection of dishonest recommenders. General Terms Mobile Ad hoc Networks, Trust Management framework, Uncertainty reasoning.

Reliable Trust Computation model in Vehicular ad-hoc Network

This paper concerns about the various implemented work has been studied and analyzed to form a new survey on trust model to VANET. In this paper it is observed that there are lot of new techniques are possible to form a new trust model in VANET to provide better security with trust concern over the entire environment of trust management in VANET. This work concerns of entire trust calculation work which has been done yet over it. Here summarizing the various trust models, various security requirements, issues over it. Keywords—Trust; VANET; Direct and indirect trust computation.

A Comprehensive Trust Model Based on Reputation and Fuzzy Theory

In Ubiquitous Network, core problem is how heterogeneous terminals communicate credibly between each other. We will achieve communication and “Ubiquitous” between varieties of heterogeneous terminals only if more reliable interaction through heterogeneous terminals could be made. This paper puts present a Comprehensive Trust Model based on Reputation and Fuzzy subsystems (CFMBRF), indirect recommending trust calculation method of model adopts “an fuzzy based credibility evaluation method for indirect trust computation”, then on basis of model based on reputation, import fuzzy inference subsystems, it is able to handle subjective concept such as importance of a the decisions in uncertainty region and setting result of interaction , can humanistic make decision about uncertain problem. This paper simulates network having no-fuzzy subsystem and network which have fuzzy subsystem, makes comparison between two networks, tests overhead and stability of CFMBRF. It can be seen that network using fuzzy subsystem greatly increased validity of interaction, decreased number of query for reputation vector, and proved effectiveness of CFMBRF model.

A mechanism for detecting dishonest recommendation in indirect trust computation

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.

An Effective Model for Indirect Trust Computation in Pervasive Computing Environment

The performance of indirect trust computation models (based on recommendations) can be easily compromised due to the subjective and social-based prejudice of the provided recommendations. Eradicating the influence of such recommendation remains an important and challenging issue in indirect trust computation models. An effective model for indirect trust computation is proposed which is capable of identifying dishonest recommendations. Dishonest recommendations are identified by using deviation based detecting technique. The concept of measuring the credibility of recommendation (rather than credibility of recommender) using fuzzy inference engine is also proposed to determine the influence of each honest recommendation. The proposed model has been compared with other existing evolutionary recommendation models in this field, and it is shown that the model is more accurate in measuring the trustworthiness of unknown entity.

A light-weight trust-based QoS routing algorithm for ad hoc networks

In a mobile ad hoc network (MANET), the lack of a trusted infrastructure makes secure and reliable packet forwarding very challenging, especially for providing QoS guarantee for multimedia applications. In this paper, we firstly introduce the concept of trust and QoS metric estimation into establishing a trust-based QoS model. In this model, we estimate the trust degree between nodes from direct trust computation of direct observation and indirect trust computation by neighbors’ recommendations. On the other hand, due to the NP-completeness of the multi-QoS constraints problem, we only take into account link delay as the QoS constraint requirement. Then, we design a trust-based QoS routing algorithm (called TQR) from the trade-off between trust degree and link delay. At last, by using NS2 we implement this algorithm based on AODV (Ad hoc On-demand Distance Vector). We compare its performance with AODV, Watchdog-DSR and QAODV. The simulation results show that TQR scheme can prevent attacks from malicious nodes and improve the security performance of the whole network, especially in terms of packet delivery ratio, average end-to-end delay, routing packet overhead and detection ratio of malicious nodes.