Dynamic Trust Evaluation Model Research Articles

A Dynamic Trust evaluation and update model using advance decision tree for underwater Wireless Sensor Networks

Underwater wireless sensor networks (UWSNs) are an emerging research area that is rapidly gaining popularity. However, it has several challenges, including security, node mobility, limited bandwidth, and high error rates. Traditional trust models fail to adapt to the dynamic underwater environment. Thus, to address these issues, we propose a dynamic trust evaluation and update model using a modified decision tree algorithm. Unlike baseline methods, which often rely on static and generalized trust evaluation approaches, our model introduces several innovations tailored specifically for UWSNs. These include energy-aware decision-making, real-time adaptation to environmental changes, and the integration of multiple underwater-specific factors such as water currents and acoustic signal properties. Our model enhances trust accuracy, reduces energy consumption, and lowers data overhead, achieving a 96% accuracy rate with a 2% false positive rate. Additionally, it outperforms baseline models by improving energy efficiency by 50 mW and reducing response time to 20 ms per packet. These innovations demonstrate the proposed model’s effectiveness in addressing the unique challenges of UWSNs, ensuring both security and operational efficiency goals. The proposed model effectively enhances the trust evaluation process in UWSNs, providing both security and operational benefits. These key findings validate the potential of integrating modified decision tree algorithms to improve the performance and sustainability of UWSNs.

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.

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.

An Efficient Dynamic Trust Evaluation Model for Wireless Sensor Networks

Trust evaluation is an effective method to detect malicious nodes and ensure security in wireless sensor networks (WSNs). In this paper, an efficient dynamic trust evaluation model (DTEM) for WSNs is proposed, which implements accurate, efficient, and dynamic trust evaluation by dynamically adjusting the weights of direct trust and indirect trust and the parameters of the update mechanism. To achieve accurate trust evaluation, the direct trust is calculated considering multitrust including communication trust, data trust, and energy trust with the punishment factor and regulating function. The indirect trust is evaluated conditionally by the trusted recommendations from a third party. Moreover, the integrated trust is measured by assigning dynamic weights for direct trust and indirect trust and combining them. Finally, we propose an update mechanism by a sliding window based on induced ordered weighted averaging operator to enhance flexibility. We can dynamically adapt the parameters and the interactive history windows number according to the actual needs of the network to realize dynamic update of direct trust value. Simulation results indicate that the proposed dynamic trust model is an efficient dynamic and attack-resistant trust evaluation model. Compared with existing approaches, the proposed dynamic trust model performs better in defending multiple malicious attacks.

A dynamic trust evaluation mechanism based on affective intensity computing

A complete description of trust relationship is key to construct a high precision trust model. But most of existing models not only miss the negative information and the hesitation information of trust, but also ignore the discordance between text comments and ratings. To solve the problems, a dynamic trust evaluation model based on the affect intensity is proposed. In the model, the intensity of sentimental polarities are calculated from words in comments. The corresponding relation between evaluations of trust property and the emotional intensity vector can be also described. Time series weights to distinguish the importance of transaction at different time are determined by the inverse form of exponential distribution. So the dynamic attenuation of trust can be described. To improve the polymerization ability of trust information, the local trust, the feedback trust and the overall trust are calculated by operators of fuzzy logic. The experimental results show the proposed model can effectively describe the trust relationship between nodes to identify and eliminate various malicious attacks significantly. Copyright © 2016 John Wiley & Sons, Ltd.

Dynamic trust evaluation in open networks

AbstractTrust has been widely used as an effective means of dealing with security issues in open networks. By taking into consideration of the requirement on dynamic adaptability and incorporating the factors of insufficient conglomerate and incentive mechanisms in trust evaluation, we propose in this paper a dynamic trust evaluation model based on multiple factors that is suitable for open networks. In the proposed model, trust evaluation takes into consideration of direct trust that includes historical information to improve accuracy and recommendation trust that includes a bonus-penalty factor and the reliability of evaluation to improve reliability and efficiency. The calculation of integrated trust relies on solving the problem of determining the weights between direct trust and recommendation trust, resulting in a balanced weight factor being introduced into trust evaluation. In the interactions between network entities, the balanced weight factor changes dynamically as the interactions continue bet...

Service Dynamic Trust Evaluation Model based on Bayesian Network in Distributed Computing Environment

In recent years, with the rapid development of computer network, distributed computing system has a very vast application prospect and potential utility value, which opens up a wealth of opportunities for different applications. With the characteristics of dynamic, heterogeneity, distribution, openness, voluntariness, uncertainty and deception, how to obtain trustworthy computing resource becomes a key issue in large-scale distributed computing research. Therefore, with considering the complex characters of trust in distributed computing environment, firstly, we construct STE architecture to rank and observe trust, which includes STE Broker, Monitoring and STE Catalogue. Secondly, a more comprehensive dynamic trust evaluation model is constructed based on Bayesian network. Finally, we apply simulation platform to imitate trust evolution process and collect related data, and the proposed method has been serviced in complex simulation system, and the results have indicated that the model is unbiased and effective. The first part is the research status and related problems. The second part is the establishment of an evaluation model. The last part is the experimental analysis and conclusion.

Modeling Dynamic Trust and Risk Evaluation Based on High-Order Moments

This paper proposes a dynamic trust and risk evaluation model based on high-order moments. The credibility of an entity is measured with trust degree and risk value comprehensively. Firstly, considering the dynamic and time decay characters of trust, a time attenuation function is defined, and direct trust is further expressed. Subsequently, in order to improve the accuracy of feedback trust, a filter mechanism is constructed to eliminate the false feedback, combining coefficient of skewness with hypothesis test. More importantly, the weights of direct trust and feedback trust are derived subjectively and adaptively with the moments and frequency of direct interactions. Furthermore, risk is evaluated with direct risk and feedback risk, which are obtained by mainly using coefficient of variation and coefficient of kurtosis. Risk value can be used to measure the stability of providing services. Simulation results show that the proposed model not only has high accuracy, but also resists effectively collusive attacks and strategic malicious behaviors.

PHAT: A Preference and Honesty Aware Trust Model for Web Services

Trust is one of the most critical factors for a service requestor when selecting a service from a large pool of candidate services. However, existing web service trust models either do not consider users' preferences for different quality of service (QoS) attributes, or ignore the impact of vicious ratings on trust evaluation. To address these gaps, PHAT, a dynamic trust evaluation model with dual consideration of users' preferences and false ratings, is proposed in this paper. The model introduces an approach to automatically mine users' preferences from their requirements. The preferences are then used to determine the weight of each QoS attribute when integrating trust into multi-dimensional QoS attributes. The “local” trust on a service is derived by combining the trust on QoS attributes and the user's subjective ratings. Then, the users are divided into different groups according to their QoS preferences, and the honesty of each group is assessed by filtering out dishonest users based on a hybrid approach combining rating consistency clustering with an average method. Finally, the weight on ratings is dynamically adjusted according to the results of honesty assessment when calculating the global trustworthiness, namely, the reputation of a service. The proposed model is evaluated with real-world QoS data, and the results indicate that PHAT works well on personalized evaluation of trust, and can effectively dilute the influence of malicious ratings.

A Dynamic Trust Evaluation Model for Internet Computing

Trust mechanism has been identified as one of key areas in the field of Internet security. Our objective in this paper is to develop a dynamic trust evaluation model for Internet computing. The proposed scheme divides the recommendation trust into the direct recommendation trust and the indirect recommendation trust, and considers variety of factors to calculate the two types of recommendation trust degree. Simulation results show that, compared to the existing trust model, the model can effectively improve the accuracy of trust evaluation, and provide a better capacity of resisting malicious entities.

A New Approach of Trust Relationship Measurement Based on Graph Theory

The certainty trust relationship of network node behavior has been presented based on graph theory, and a measurement method of trusted-degree is proposed. Because of the uncertainty of trust relationship, this paper has put forward the random trusted-order and firstly introduces the construction of trust relations space (TRS) based on trusted order. Based on all those above, the paper describes new method and strategy which monitor and measure the node behavior on the expectancy behavior character for trusted compute of the node. According to manifestation of node behavior and historical information, adjust and predict the trusted-order of node behavior. The paper finally establishes dynamic trust evaluation model based node behavior characters, and then it discusses the trusted measurement method which measures the connection and hyperlink for node behavior of network in trust relationship space.