Trust Assessment Model Research Articles

Adaptive multi-granularity trust management scheme for UAV visual sensor security under adversarial attacks

The big data provided by unmanned aerial vehicle (UAV) visual sensors offers essential information resources for activities across various industries. However, various adversarial threats are inevitable throughout the lifecycle of data generation, transmission, and utilization, leading to serious security risks. Trust assessment of visual sensors is a prerequisite for securing UAVs, but the multidimensionality of the trust elements and the uncertainty of the evidence limit its practical application. To advance this research, we innovatively propose a trust management scheme based on multi-granularity evidence fusion within the framework of belief functions (BFs) theory to adaptively respond to both known and unknown threats. We first propose a direct trust assessment model for known threats, which constructs multidimensional coarse-grained trust elements (MCTEs) and integrates multiple lightweight sub-models for basic belief assignment (BBA) to meet the need for fast response. Then, to address the unknown threats, we introduce pre-trained models to build multidimensional fine-grained trust elements (MFTEs) to construct trust recommendation models for indirect trust assessment for visual sensors. In addition, to accurately characterize the trustworthiness of visual sensors, we also introduce a BBA-weighted fusion method to achieve more reasonable trust aggregation by weakening highly conflicting evidence sources. Finally, to validate the effectiveness of the proposed method, we conducted a comprehensive trust assessment and security experiment on UAV aerial images. The results indicate that the proposed method demonstrates excellent performance and is beneficial for enhancing UAV security in adversarial attack scenarios.

Using attentive temporal GNN for dynamic trust assessment in the presence of malicious entities

Trust networks are built on the premise that there is a network of trust among entities. Precisely evaluating pairwise trust in such networks is essential for enhancing the quality of service in various applications, including online marketing, recommender systems, and IoT-based applications. However, it remains challenging to consider dynamic trust and account for malicious behavior in a scalable and effective manner.Recently, Graph Neural Networks (GNN)-based methods have emerged as powerful and scalable tools for trust estimation. Nevertheless, they lack thorough consideration for the time-dependent nature of trust, especially in the presence of malicious behaviors, leading to inaccurate trust predictions. To address this gap, we propose a novel trust assessment model called MATA, which handles the time dependency of trust and exhibits robustness against malicious behaviors. MATA leverages an extended Graph Convolutional Network (GCN) integrated with Recurrent Neural Networks (RNNs) and an attention mechanism to jointly capture time dependencies and trace entities’ behavior over time in complex networks. Additionally, by clustering the learned trust representations over time, we assign reputation values to entities. This allows us to monitor their dynamic behavior and mitigate potential adverse effects on trust assessment.In our experiments on real-world datasets, MATA outperforms the state-of-the-art methods by up to 8.4 % in accuracy. This improvement is a testament to the effectiveness of the proposed approach. Furthermore, our work enhances the robustness of trust assessment models, as evidenced by consistently higher performance metrics in the face of various trust-related attacks.

A Consumer Trust Assessment Model for Online Shopping Based on Fuzzy Fusion Decision-Making

With the rapid development of e-commerce, online shopping has become an indispensable part of people's daily lives. However, consumers often face trust issues during online shopping, such as product quality and seller integrity, which directly impact their shopping experience and purchasing decisions. Therefore, accurately assessing consumer trust has become a crucial task. This study first constructs a consumer trust assessment system, analyzing and selecting key factors related to consumer trust, and establishes a model for assessing consumer trust for online shopping. Subsequently, we propose an assessment method based on text mining and deep learning sentiment analysis techniques to extract consumer sentiment information from specified consumer reviews. Furthermore, through fuzzy decision-making fusion strategy, we integrate sentiment information from the dimensions of quality assurance, reliability, and responsiveness to enhance the accuracy of the assessment.

IoT trust aggregation using hybrid outlier detection and consensus

Trust modelling and management strategy used identify and mitigate threats by malicious devices rely on peer recommendations to compute trustworthiness. Aggregating opinions from independent devices is crucial in such recommendation-based systems to arrive at a consensus for decision making. Existing aggregation techniques like arithmetic mean, geometric mean (weighted/non-weighted), and maximum/minimum functions ignore the risk of biased and uncertain recommendations. To encounter such vulnerabilities, we propose a novel trust assessment model, outlier and uncertain recommendation-based trust management (OUR-Trust). It uses an outlier elimination and similarity-based scheme to evaluate the recommender's credibility before aggregation for consensus and decision making. The model employs revised Dempster-Shaffer combination rule for aggregation, which considers the uncertainty factor. Effectiveness of proposed approach is analysed for a dynamic and heterogeneous IoT network of dynamic and heterogeneous devices. OUR-Trust is validated for storage, power-efficiency, and scalability in terms of convergence time for more extensive IoT networks that employ recommender systems.

Trusted routing protocol for federated UAV ad hoc network

PurposeIn the final step, the trust model is applied to the on-demand federated multipath distance vector routing protocol (AOMDV) to introduce path trust as a foundation for routing selection in the route discovery phase, construct a trusted path, and implement a path warning mechanism to detect malicious nodes in the route maintenance phase, respectively.Design/methodology/approachA trust-based on-demand multipath distance vector routing protocol is being developed to address the problem of flying ad-hoc network being subjected to internal attacks and experiencing frequent connection interruptions. Following the construction of the node trust assessment model and the presentation of trust evaluation criteria, the data packet forwarding rate, trusted interaction degree and detection packet receipt rate are discussed. In the next step, the direct trust degree of the adaptive fuzzy trust aggregation network compute node is constructed. After then, rely on the indirect trust degree of neighbouring nodes to calculate the trust degree of the node in the network. Design a trust fluctuation penalty mechanism, as a second step, to defend against the switch attack in the trust model.FindingsWhen compared to the lightweight trust-enhanced routing protocol (TEAOMDV), it significantly improves the data packet delivery rate and throughput of the network significantly.Originality/valueAdditionally, it reduces the amount of routing overhead and the average end-to-end delay.

Trust Assessment Model Based on a Zero Trust Strategy in a Community Cloud Environment

The adoption of Cloud Computing services in everyday business life has grown rapidly in recent years due to the many benefits of this paradigm. The various collaboration tools offered by Cloud Computing have eliminated or reduced the notion of distance between entities of the same company or between different organizations. This has led to an increase in the need to share resources (data and services). Community Cloud environments have thus emerged to facilitate interactions between organizations with identical needs and with specific and high security requirements. However, establishing trust and secure resource sharing relationships is a major challenge in this type of complex and heterogeneous environment. This paper proposes a trust assessment model (SeComTrust) based on the Zero Trust cybersecurity strategy. First, the paper introduces a community cloud architecture subdivided into different security domains. Second, it presents a process for selecting a trusted organization for an exchange based on direct or recommended trust value and reputation. Finally, a system for promoting or relegating organizations in the different security domains is applied. Experimental results show that our model guarantees the scalability of a community cloud with a high success rate of secure and quality resource sharing.

L-RTAM: Logarithm based reliable trust assessment model for WBSNs

In has been considered that the sensor network’s safety measures have been built upon a non-realistic, trusted environment. After analyzing the trust models for wireless body sensor networks (WBSNs), it has been found that trust models should be distributed, lightweight, and robust against various vulnerabilities. They are also facing issues regarding computation overhead. Moreover, there is a threat of internal attacks over WBSNs. These attacks may be an on-off attack, data modification, message disclosure, collusion attack, bad-mouthing, brute force, and denial of service attack. Thus, this research paper considers three issues: network internal security, resource management, and spiteful node detection. The Proposed dynamic and trustworthy lightweight trust assessment scheme (RTAM) for WBSNs would play a significant role in the appropriate usage of resources and minimize the computational overhead. RTAM is a scalable trust model that incorporates logarithm based direct trust, indirect trust, and success ratio based data trust. It would be safe from a different type of attacks. To check the malicious activities that have been performed in nodes, some malicious nodes from the total nodes have been managed by the proposed network.

Independent Component Analysis for IoT Services Using Machine Learning

Machine learning is an implementation of Artificial Intelligence (AI) that allows devices to learn and develop independently without having to be directly programmed. Machine learning is concerned with developing computer programmes that can access data and learn on their own. The introduction of the internet has revolutionised the use of machine learning in today's century. The new scheme, which seeks to create a quantifiable trust assessment model, then measures specific confidence qualities numerically. An epic calculation based on AI standards is conceived to portray the separated certainty highlights and join them to deliver a last trust characteristic to be utilized for dynamic. One of the methods used is the Generic Trust Computational Model (GTCM). It's a prototype that displays relevant details about the confidence acquisition and evaluation process using three Trust Metrics (TMs): experience, practise, and reputation. The Machine Learning Model uses the Principal Component Analysis (PCA) calculation, which depends on Singular Value Decomposition (SVD), to lessen the N measurements to two for perception purposes. In a number of implementations, Independent Component Analysis (ICA) has outperformed standard PCA. When Principal Component Analysis failed to differentiate eye artefacts from brain signals, particularly when their amplitudes were identical, it was used to exclude them from the ElectroEncephaloGram (EEG).

Impact of social media campaigns on consumers

The proliferation of social media has given consumers additional channels through which to make online social connections. Online communities and other types of social media allow users to share content and build relationships with one another. Analysis of the internet may also reveal the potential advantages for companies. Based on the Trust Assessment Model (TAM) and the current research on trust and social media, a multidisciplinary structure has been established. The role that social media had in the development of "social commerce," or online purchasing as a social activity. Consumers are more likely to trust businesses and make purchases after engaging with them on social media, according to study results. A clear correlation between trust and purchase intent is also shown. A site's PU, or its perceived utility, is also cited as a consideration. Implications, limits, and suggestions for future studies are discussed in the paper's conclusion.

Resource management in client-server computing systems based on trust assessment

The article describes a trust assessment model and a method based on it for managing client access to scarce resources in a client-server computing system. Trust in a client is the degree to which it meets the standard. The trust assessment score is based on an ordinal preference scale with a limited normalized weight of criteria. The model allows you to get a numerical assessment of the client’s compliance with a given set of criteria. The method of managing computer system resources is based on a controlled impact on the communication channel between the client and the server of the system. This method describes how to select a threshold value for the trust assessment score. The method allows you to reasonably limit the total number of clients in the system and thereby increase the probability of access to scarce computing resources of the server for trusted clients.

Credibility Assessment of Mobile Social Networking Users Based on Relationship and Information Interactions: Evidence From China

Personalized, diversified and decentralized mobile social networking platforms provide users with an environment for accessing, sharing, distributing information and communicating emotionally. In this virtual social space, effective identification of trusted nodes can assist social networking users to make interactive strategies, and provide enterprises with scientific basis for the social interactive marketing decisions. Therefore, trust assessment plays a crucial role in establishing social connections among mobile social networking users. In this paper, based on the social relationship and information interactive data between the users of Sina Weibo, four factors, the intensity of social relations, the sphere of social influence, the information value and the control of information transmission are taken into account to build the trust assessment model. Secondly, the trust between users is quantified by node credibility and entropy weight method is employed to calculate the four factors' contribution to the node credibility respectively. Finally, the credibility of each node in Sina Weibo is calculated to identify the trusted nodes.

A Trust Assessment Model Based on Recommendation and Dynamic Self-adaptive in Cloud Service

Cloud computing is a completely distributed sharing platform, through which users can request personalized cloud services. With the continually expansion of cloud services scale, more and more service providers are offering services with similar functions but different qualities. Users are faced with the problem of selecting the most suitable cloud services from numerous cloud services. In this paper, a trust assessment model based on recommendation and dynamic self-adaptive in cloud service is proposed. In the trust assessment model, trust is divided into direct trust and indirect trust, which can make the evaluation of trust value more accurate. In addition, a recommendation mechanism is proposed in this paper, the third-party nodes make recommendations according to the actual situation, and accept reward or punishment automatically after interacting. Experiment shows that the model can accurately evaluate the trust between nodes, and effectively resist the attack of malicious nodes.

Assessment of organization’s information security on the criterion of confidence

Currently, the protection of information remains a pressing issue, and the most common approaches to its assessment are verification and risk-oriented method. However, the information security metrics in the relevant methodologies based on these approaches are insufficiently informative, since they take into account only objective aspects of security, completely ignoring the subjective ones. Therefore, they do not allow the development of judgments based on the state of confidentiality, integrity and availability of information and the organization’s information security level as a whole. For that reason, there is a necessity to develop a methodological apparatus for assessing the organization's information security, taking into account objective and subjective aspects of security. The article proposes the approach to assessing information security on the basis of the criterion of confidence that an organization implements its adopted security policy. Assessment of confidence includes assessment of the credibility of organization’s information security, the quality of the trust assessment model and the background of the persons who conducted such an assessment and knowledge assessment regarding threats. As an indicator of confidence, the utility indicator is used as the value of the generalized Harrington’s desirability function. The proposed approach to assessing the organization's information security is fairly simple to be implemented and can be used as a pilot to develop appropriate methods for assessing the security of organizations of various forms of ownership.

Machine Learning Based Trust Computational Model for IoT Services

The Internet of Things has facilitated access to a large volume of sensitive information on each participating object in an ecosystem. This imposes many threats ranging from the risks of data management to the potential discrimination enabled by data analytics over delicate information such as locations, interests, and activities. To address these issues, the concept of trust is introduced as an important role in supporting both humans and services to overcome the perception of uncertainty and risks before making any decisions. However, establishing trust in a cyber world is a challenging task due to the volume of diversified influential factors from cyber-physical-systems. Hence, it is essential to have an intelligent trust computation model that is capable of generating accurate and intuitive trust values for prospective actors. Therefore, in this paper, a quantifiable trust assessment model is proposed. Built on this model, individual trust attributes are then calculated numerically. Moreover, a novel algorithm based on machine learning principles is devised to classify the extracted trust features and combine them to produce a final trust value to be used for decision making. Finally, our model's effectiveness is verified through a simulation. The results show that our method has advantages over other aggregation methods.

Node Mutual Authentication Based on Trust-Assessment Model in the IoT-Sensor Networks

Node Mutual Authentication Based on Trust-Assessment Model in the IoT-Sensor Networks

CCDEA: Consumer and Cloud – DEA Based Trust Assessment Model for the Adoption of Cloud Services

Abstract Knowing the trust level of cloud service providers is a significant issue in the field of cloud computing for privacy and security reasons. The idea of this paper is to build up a Consumer and Cloud-Data Envelopment Analysis (CCDEA) trust assessment model for evaluating cloud services in two stages. In first stage, the believability index of each cloud Consumer (C) is calculated. The second stage incorporates Cloud-Data Envelopment Analysis (C-DEA) model for the trust assessment of cloud services from the viewpoint of cloud consumers. Several experiments were conducted and the results were analyzed to show the stability of our method in measuring the relative efficiency and effectiveness of cloud services through ranking mechanism.

2S-FAT-Based DLS Model for Cloud Environment

In the current era of technology, cloud computing is an indispensable jargon due to its theoretically boundless resources. But from consumer’s point of view, knowing the trust level of cloud service providers is significant for privacy and security reasons. Toward trusted cloud computing, we first propose a Two-Stage Fuzzy and Ant colony optimization based Trust assessment model for a cloud environment which is then integrated with an existing Dynamic Level Scheduling (DLS) algorithm for scheduling and executing consumers’ jobs. To demonstrate the effectiveness of our model, a benchmark is developed in Cloudsim and experiments are conducted against the metric-based DLS model. Results are analyzed in terms of trust index, undecidability, success rate, average schedule length, execution time, precision and recall values.

Trust assessment of security for e-health systems

The expansive connectivity of emerging information systems has set the stage for pervasive access to healthcare services via e-health systems for selecting the best possible healthcare services. Emerging systems are expected to be highly dynamic open environments connecting diverse number of healthcare services and autonomous entities that are autonomous agents or software applications representing patients. Entities in such dynamic environments may have different security needs from e-health systems raising the challenge of trust computations regarding security. In this research, we proposed a trust assessment model of an e-health service from the viewpoint of an entity. The model contains a comprehensive architecture applicable to different types of entities, and a novel set of trust assessment metrics may be used to assess a specific property of a security system (i.e. partial metrics) or all properties (i.e. total metrics). The simulation based evaluation of proposed model in the context of a Hospital Online Appointment Service has shown that the proposed model provides better trust computation results than existing trust models for e-health systems. Furthermore, the entities are also able to assess the trust even with incomplete security information.

The growth of population in the Chesapeake colonies: A comment

The expansive connectivity of emerging information systems has set the stage for pervasive access to healthcare services via e-health systems for selecting the best possible healthcare services. Emerging systems are expected to be highly dynamic open environments connecting diverse number of healthcare services and autonomous entities that are autonomous agents or software applications representing patients. Entities in such dynamic environments may have different security needs from e-health systems raising the challenge of trust computations regarding security. In this research, we proposed a trust assessment model of an e-health service from the viewpoint of an entity. The model contains a comprehensive architecture applicable to different types of entities, and a novel set of trust assessment metrics may be used to assess a specific property of a security system (i.e. partial metrics) or all properties (i.e. total metrics). The simulation based evaluation of proposed model in the context of a Hospital Online Appointment Service has shown that the proposed model provides better trust computation results than existing trust models for e-health systems. Furthermore, the entities are also able to assess the trust even with incomplete security information.