Trust Evaluation Model Research Articles

An interval type‐2 fuzzy trust evaluation model in social commerce

AbstractTrust is crucial for purchasing decisions in social commerce. However, inexperienced users may not have a direct trust relation to experienced users in practice. Besides, users tend to give their trust degrees to others with linguistic labels rather than crisp values. To evaluate the trust degree for inexperienced users to experienced ones, we propose an interval type‐2 fuzzy trust evaluation model in this paper. Interval type‐2 fuzzy linguistic labels are used to represent trust degree among users. An interval type‐2 fuzzy Algebraic t‐norm is addressed to compute propagative trust degrees. Considering the effect of trust path length, the induced ordered weighted averaging (IOWA) operator is extended to aggregate the interval type‐2 fuzzy trust degrees obtained from multiple paths. In addition, the final interval type‐2 fuzzy trust degree is transferred into the corresponding linguistic label to help users make decisions more naturally. Finally, a case study in social commerce and a related comparison are given to verify the effectiveness of the proposed model.

Trust-Evaluation-Based Intrusion Detection and Reinforcement Learning in Autonomous Driving

With ever-increasing penetration of autonomous driving vehicles (ADVs), security becomes of significant essence to autonomous vehicular networks (AVNs). On one hand, although cryptographic systems help deal with various attacks, they cannot resolve inside attacks from compromised or malfunctioning ADVs. On the other hand, ADVs with higher automated levels are easier to attack because the automated components onboard are vulnerable to inside attacks. These issues can be mitigated well by trust-based intrusion detection. Therefore,this article presents an efficient trust-based intrusion detection framework for AVNs. First, we propose a novel trust evaluation model for ADVs, in which all the trust evaluation information of a given ADV is utilized to compute its trust value. Then, based on the trust evaluation model, a two-level intrusion detection framework is presented. In the framework, the trustworthiness of an accident or attack warning is established not only based on trust evaluation with the coverage of a roadside unit (RSU), but also the information exchanged between RSUs through the cloud server. Afterward, we propose a reinforcement-learning-based incentive mechanism to stimulate ADVs to report warnings. Through the case study, the proposed framework outperforms the conventional mechanism and can reach a higher warnin

Trust evaluation model with entropy-based weight assignment for malicious node\u2019s detection in wireless sensor networks

Trust management is considered as an effective complementary mechanism to ensure the security of sensor networks. Based on historical behavior, the trust value can be evaluated and applied to estimate the reliability of the node. For the analysis of the possible attack behavior of malicious nodes, we proposed a trust evaluation model with entropy-based weight assignment for malicious node’s detection in wireless sensor networks. To mitigate the malicious attacks such as packet dropping or packet modifications, multidimensional trust indicators are derived from communication between adjacent sensor nodes, and direct and indirect trust values will be estimated based on the corresponding behaviors of those sensor nodes. In order to improve the validity of trust quantification and ensure the objectivity of evaluation, the entropy weight method is applied to determine the proper value of the weight. Finally, the indirect trust value and direct trust value are synthesized to obtain the overall trust. Experimental results show that the proposed scheme performs well in terms of the identification of malicious node.

Trust Models in Grid Computing: A Review

Background:Grid computing relates to a pool of resources to be shared by users in Grid Environment. Security of the resources from users and vice-versa is a significant issue. This is where the notion of trust comes into existence. A number of researchers have proposed models for evaluation of trust in grid computing, but they fail to spot one or the other parameters for trust evaluation. The essence of trust models in grid computing is that they offer autonomic trust management. An autonomic trust model has been patented by Z. Yan and C. Prehofer in 2009. Another patent was published by Anna University in 2010 to evaluate the trustworthiness of a resource provider in Grid environment.Objective:This paper firstly focuses and illustrates these essential parameters. Based on these parameters, further, a comparison of some existing models for trust evaluation is shown. Finally, common parameters missed out by various models have been highlighted giving way for improvements of Trust model.Methods:A Trust evaluation model has been proposed by us previously based on a number of real-world trust evaluation parameters. This model sees trust as a three-dimensional entity. Trust is based on Dempter Shafer’s theory in which trust is calculated mathematically.Results:Software trust needs to be calculated mathematically. There are a large number of real-world parameters that need to be included for evaluating trust.Conclusion:As trust models in research are based on simulation techniques, so it is important to include real-world factors that affect trust value of one entity on other. Some of those parameters, missed by most of the models have been identified for inclusion in future trust models.

An intelligent trust model for hybrid DDoS detection in software defined networks

SummarySoftware Defined Networks (SDNs) have been extensively studied in recent years. The centralised and programmable controller also brings many security challenges. As a conventional attack with the purpose of destruction, Distributed Denial of Service (DDoS) is still a threat for Software Defined Networks (SDNs). There is a lack of trust evaluation and management mechanism between the OpenFlow switches in SDNs. Therefore, in this paper, we propose a trust evaluation and management model, namely, the Intelligent Trust Model (ITM). In this framework, the Extreme Learning Machine (ELM) is applied to detect hybrid DDoS in Software Definition Networks (SDNs). Our model (ITM) can update the trust value of the OpenFlow switches in real time and respond quickly to different types of DDoS attacks. Compared with others, the experiment results show that our model can provide more efficient detection with high detection accuracy and low false positive rate for the hybrid DDoS attack. At last, in our proposal, OpenFlow switches with higher trust have a relatively higher priority. Therefore, we solve the flow conflict issue in the infrastructure layer.

Hospital evaluation mechanism based on mobile health for IoT system in social networks

The uneven distribution of medical resources is a serious problem in developing countries. Those seeking timely treatment have difficulty choosing the right hospital. To found sustainable development with medical system, this paper establishes a model of the hospital confidence evaluation index by combining national evaluation and a third-party evaluation. The model is applied to a social network. Users from any region can use the model through APP in IoT, a hospital analysis index query, which selects the best hospital for diagnosis and treatment. The model can locate different personnel characteristics by modifying the control variables. Establishing a medical system with big data provides good model characteristics. Effective data analysis through large data users in the sample is established to provide the most effective hospital recommendation, which is a good solution to the selectivity problem. The contributions in this works are: (1) Models of initial trust and hospital evaluations are established by combining national and third-party assessments; (2) the initial trust evaluation model is modified and optimized by establishing control variables; (3) the trust evaluation mechanism of users in social networks is obtained through big data sampling and model analysis, and the balanced distribution of the medical staff is realized.

Formulating Knowledge-Based Cloud Identity Selection

ABSTRACT Cloud identity is critical in the adoption of cloud computing. Identity providers provide identity as a service for the cloud customers to facilitate the access to resources, but often provide no informed choice of the supply arrangements. The cloud customer requires a say in the elements of service that impact their expectations and quality of service – particularly in relation to the security and privacy of information. In this paper, we provide a novel software solution for addressing the problem. It allows for selecting a most trustable provider, in terms of security, privacy, risk, and standards. The application architecture uses a trust evaluation model as a third-party agency for informing the service user of evidence for and against a choice. A case study and application build demonstrate the feasibility and usefulness of the proposed method in a real cloud environment.

A service satisfaction-based trust evaluation model for cloud manufacturing

ABSTRACT In the cloud manufacturing service model, a good trust relationship among trading entities including manufacturing service providers and consumers is the basis for service transactions. To characterize the cooperative trust effectively, the evaluation index system was built to quantize the service satisfaction by monitoring the data of historical service evaluation, and then a service satisfaction-based trust evaluation model was proposed. The direct satisfaction, the friend recommendation satisfaction and the platform satisfaction were integrated into the comprehensive trust. To improve the reliability of the proposed model, the time decay function was utilized to characterize the change of service satisfaction over time. Meanwhile, the comprehensive trust was corrected by using the service satisfaction volatility, which was taken as a mechanism of reward and punishment in the long-term transaction process. The experimental results verified that the model reflected the subjective preference of consumers and showed good accuracy and efficiency, especially in the case of big transaction data.

Secure Routing Protocol based on Multi-objective Ant-colony-optimization for wireless sensor networks

To solve the problem of achieving the maximum network security goal with lower energy consumption in wireless sensor networks, this paper proposes a Secure Routing Protocol based on Multi-objective Ant-colony-optimization (SRPMA) for wireless sensor networks. The ant colony algorithm is improved to be a multi-objective routing algorithm with considering the residual energy of nodes and the trust value of a rout path as two optimization objectives, in which a rout path is produced through the multi pheromone information and the multi heuristic information consisting of two objective functions. The node trust evaluation model is established by using an improved D-S evidence theory with confliction preprocessing to evaluate nodes trust degree. The multi-objective routing result is obtained by using the Pareto optimal solution mechanism by using the external archive method with a crowding distance criterion. The simulation results conducted with NS2 show that the proposed algorithm can achieve desired performance against the black hole attack in WSN routing.

A Cloud Service Trust Evaluation Model Based on Combining Weights and Gray Correlation Analysis

Cloud services are cloud computing resources and applications deployed on the Internet or cloud computing platform, and users can access the required cloud services at any time. However, users face the diversity and complexity of quality of service (QoS) when evaluating and selecting cloud services. Therefore, it is important to study and establish an effective and objective trust model to improve user satisfaction and interaction success rate. In this paper, a model based on combining weights and gray correlation analysis is proposed. Firstly, direct trust, recommendation trust, and reputation together form a comprehensive trust, resulting in a more accurate overall trust. Second, rough set theory and analytic hierarchy process (AHP)-based method are used for the direct trust. Meanwhile, the degree of similarity recommendation trust is calculated by a gray relational analysis method. In order to ensure the accuracy of direct trust, this paper proposes a dynamic trust update mechanism. Finally, the simulation experiment is carried out to verify that the cloud services trust evaluation model (CSTEM) is more robust than the other three methods. It protects against malicious entities; at the same time, it can increase user satisfaction and interaction success rate.

Dynamic trust evaluation model based on bidding and multi-attributes for social networks

Mutual trust is the most important basis in social networks. However, many malicious nodes often deceive, collaboratively cheat, and maliciously recommend other nodes for getting the more benefits. Meanwhile, because of lacking effective incentive strategy, many nodes are neither to evaluate nor to recommend. Thus, malicious actions have been aggravated in social networks. To solve these issues, we designed a bidding strategy to incentivise nodes to do their best to recommend or evaluate service node. At the same time, we also employed TOPSIS method of selecting a correct service node for system from networks. To guarantee reliability of service node selected, we brought recommendation time influential function, service content similarity function and recommendation acquaintance function into the model to compute general trust of node. Finally, we gave an update method for trust degree of node and experiments analysis.

A trust-based evaluation model for data privacy protection in cloud computing

For high quality and privacy protection problem, this paper proposes a trust-based evaluation model for data privacy protection in cloud computing (TEM-DPP). In order to make the final trust evaluation values more practical, the model introduces the comprehensive trust evaluation. The comprehensive trust is composed of direct trust and recommend trust. Services attribute and combining weights-based methods are used to calculate the direct trust, reflecting the direct trust timeliness and rationality. In order to protect data security, we propose a data protection method based on normal cloud model for data privacy protection. Then, the customer satisfaction, decay time, transaction amount and penalty factor will be used to update the direct trust. Simulation results are showed that cloud services trust evaluation model can not only adapt to the dynamic changes in the environment, but also ensure the actual quality of service. It can improve the service requesters' satisfaction and has certain resilience to fraud entities.

Dynamic trust evaluation model based on bidding and multi-attributes for social networks

Mutual trust is the most important basis in social networks. However, many malicious nodes often deceive, collaboratively cheat, and maliciously recommend other nodes for getting the more benefits. Meanwhile, because of lacking effective incentive strategy, many nodes are neither to evaluate nor to recommend. Thus, malicious actions have been aggravated in social networks. To solve these issues, we designed a bidding strategy to incentivise nodes to do their best to recommend or evaluate service node. At the same time, we also employed TOPSIS method of selecting a correct service node for system from networks. To guarantee reliability of service node selected, we brought recommendation time influential function, service content similarity function and recommendation acquaintance function into the model to compute general trust of node. Finally, we gave an update method for trust degree of node and experiments analysis.

Social big data oriented trust situation risk model based on individual behavior

ABSTRACTThe individual's social behavior is diverse in the big data environment, which makes the general trust evaluation model to take into account inaccuracy and causes some new problems for individual trust. Based on this, we present the trust situation risk model based on individual behavior oriented social big data. First, we use Multi data fusion to get the result of trust evaluation, then fuse the mass function of the individual trust associated with trust evaluation with the evaluation results by using the theory of D-S. Next, we get the probability of the individual appearing as distrust situation. Each related individual distrust situation is obtained by the fusion with the situation factor. According to each related individuals' trust situation, we can get the fusion of weight about each individual participating in the trust evaluation. On the basis of the above, the trust model evaluating the individual behavior is developed, the experiment shows the performance of the proposed model in this paper.

Data mining of customer choice behavior in internet of things within relationship network

Internet of Things has changed the relationship between traditional customer networks, and traditional information dissemination has been affected. Smart environment accelerates the changes in customer behaviors. Apparently, the new customer relationship network, benefitted from the Internet of Things technology, will imperceptibly influence customer choice behaviors for the cyber intelligence. In this work, we selected 298 customers' click browsing records as training data, and collected 50 customers who used the platform for the first time as research objects. and use the smart customer relationship network correspond to cyber intelligence to build the customer intelligence decision model in Internet of Things. The results showed that the MAE (Mean Absolute Deviation) of the customer trust evaluation model constructed in this study is 0.215, 45% improvement over the traditional equal assignment method. In addition, customer's consumer experience can be enhanced with the support of data mining technology in cyber intelligence. Our work indicated the key to build eliminates confusion in customer choice behavior mechanism is to establish a consumer-centric, effective network of customers and service providers, and to be supported by the Internet of Things, big data analysis, and relational fusion technologies.

A service trust evaluation model using clustering fuzzy inference for guiding network service selection

SummaryThe evolution of distributed and virtualized network services makes service selection difficult, as service providers and their links are becoming open and random. The trust degree of service providers is considered as an effective guidance, but it is unmethodical to establish and maintain a clear and stable trust relationship between them. Traditional solutions of service trust evaluation are not comprehensive and accurate enough, because they generally do not take randomness and fuzziness into account. In this context, a model of service trust evaluation based on clustering fuzzy inference for guiding network service selection is proposed in this paper. Four clustering evaluation indexes are determined, and an evaluation mechanism is established based on the fuzzy membership function. The valuation process is time‐aware, and the fuzzy knowledge base can be iteratively updated to keep the trust degree fresh. Simulation experiments illustrate the feasibility of the proposed model and indicate the superiority of greater performance compared with other similar solutions.

Fine-grained Trust-based Routing Algorithm for Wireless Sensor Networks

The trust routing protocol is an important way to solve the security of wireless sensor networks. In previous work, nodes’ trust evaluation granularity is coarse, and routing trust evaluation model is relatively insufficient. In this paper, we propose a high-reliability trust evaluation model for secure routing based on combination inside states of a node with outside interaction behaviors between nodes. Firstly, we leverage a Markov chain prediction model with inside four states to assess trust degree of a routing node. The abnormal node could be found through changes of node power, traffic, response time and network delay. However, a compromised routing node self can change its inside states, so we explore the interaction behaviors between nodes to find abnormal nodes and use an improved Bayesian model to calculate the trust degree of nodes. Finally, this paper proposes a novel trust routing working principle based on a fine-grained trust model. NS2 (Network Simulator version 2) results show that our routing trust evaluation scheme can effectively identify the abnormal and malicious nodes, and reduce the harm caused by malicious nodes to wireless sensor networks.

Trust Management in Web Services for Prediction and Selection based on Trust Evaluation Model

Trust Management in Web Services for Prediction and Selection based on Trust Evaluation Model

An emerging intelligent optimization algorithm based on trust sensing model for wireless sensor networks

Due to the limitation of battery power, processing capacity, and storage, the sensor nodes are easy to be captured, destroyed, or attacked in an open environment. As a result, the security and reliability of data transmission cannot be guaranteed. In order to resist the internal attacks from malicious nodes, an ant colony optimization algorithm for secured routing based on trust sensing model (ACOSR) in wireless sensor networks is proposed. Firstly, a reliable evaluation model of trust perception is presented, which can estimate the node’s trust value derived from its behavior to identify or isolate the malicious nodes effectively. The penalty function and regulator function are applied to reflect the effect of state changes on the trust value according to the node’s behavior in the process of the communication. Secondly, the trust evaluation model is introduced into the ant colony routing algorithm to improve the security for data forwarding. The simulation results show that the proposed algorithm has improved the performance significantly in terms of packet loss rate, end to end delay, throughput, and energy consumption and demonstrates good resistance to black hole attack.

Trust evaluation model of cloud user based on behavior data

In the process of using the cloud platform, how to ensure the safety of users is a matter we must concern. The user authentication can provide a certain degree of security, but when the user information was leaked, this method will not be effective. Therefore, this article proposes a trust evaluation model based on user behavior data. In this model, the user’s historical behavior will be used to construct a set of trusted behavior of the cloud users. On this basis, the direct trust of the user’s behavior can be obtained. Then, the recommendation trust can be calculated by the interaction between the users and other cloud users. Given the current historical trust, the comprehensive trust can be obtained using the weighted average method. Among them, the initial value of historical trust is set to a constant and then updated by the comprehensive trust. In order to control the user’s abnormal behavior more effectively, the suspicious threshold value and the abnormal threshold value were defined, which are used to punish the historical trust. Through the simulation of the virtual digital library cloud platform, the method can effectively evaluate the cloud users.