Trust Model Research Articles

Actor Critic Approach based Anomaly Detection for Edge Computing Environments

The pivotal role of data security in mobile edge-computing environments forms the foundation for the proposed work. Anomalies and outliers in the sensory data due to network attacks will be a prominent concern in real time. Sensor samples will be considered from a set of sensors at a particular time instant as far as the confidence level on the decision remains on par with the desired value. A “true” on the hypothesis test eventually means that the sensor has shown signs of anomaly or abnormality and samples have to be immediately ceased from being retrieved from the sensor. A deep learning Actor-Criticbased Reinforcement algorithm proposed will be able to detect anomalies in the form of binary indicators and hence decide when to withdraw from receiving further samples from specific sensors. The posterior trust value influences the value of the confidence interval and hence the probability of anomaly detection. The paper exercises a single-tailed normal function to determine the range of the posterior trust metric. The decision taken by the prediction model will be able to detect anomalies with a good percentage of anomaly detection accuracy.

Energy Efficient Secure-Trust-Based Ring Cluster Routing in Wireless Sensor Network

Typically, wireless sensor networks (WSNs) are used to monitor as well as detect different kinds of objects in realistic monitoring, where security remains as a major confront. Estimation of node trust is established to be an effectual way of enhancing the security, thus aiding in nodes collaboration and decision-making in wireless and wired networks. Nevertheless, conventional methods of trust management generally highlight on trust modeling and fail to notice the overhead issues. In this paper, a security aware ring cluster routing technique is introduced. The routing is undergone based on the multi-objectives including trust (security) parameters, energy, and distance. Here, the trust parameters include both the direct trust evaluation and indirect trust evaluation. Thereby, the lifetime of the network gets maximized even with secured manner. An innovative Self-Adaptive Deer Hunting Optimization (SA-DHO) is presented in this study because the optimization plays a significant role in selecting the neighbors as ring nodes. Finally, the superiority of the suggested approach is demonstrated in relation to various measures.

Trust-based co-operative routing for secure communication in mobile ad hoc networks

The working of a Mobile Ad hoc NETwork (MANET) relies on the supportive cooperation among the network nodes. But due to its intrinsic features, a misbehaving node can easily lead to a routing disorder. This paper presents two trust-based routing schemes, namely Trust-based Self-Detection Routing (TSDR) and Trust-based Co-operative Routing (TCOR) designed with an Ad hoc On-demand Distance Vector (AODV) protocol. The proposed work covers a wide range of security challenges, including malicious node identification and prevention, accurate trust quantification, secure trust data sharing, and trusted route maintenance. This brings a prominent solution for mitigating misbehaving nodes and establishing efficient communication in MANET. It is empirically validated based on a performance comparison with the current Evolutionary Self-Cooperative Trust (ESCT) scheme, Generalized Trust Model (GTM), and the conventional AODV protocol. The extensive simulations are conducted against three different varying network scenarios. The results affirm the improved values of eight popular performance metrics overcoming the existing routing schemes. Among the two proposed works, TCOR is more suitable for highly scalable networks; TSDR suits, however, the MANET application better with its small size. This work thus makes a significant contribution to the research community, in contrast to many previous works focusing solely on specific security aspects, and results in a trade-off in the expected values of evaluation parameters and asserts their efficiency.

The influence of trust and social identity in farmers’ intentions to report suspected emergency disease outbreaks

This study examines the influence of trust on farmers’ intentions to report suspected disease on their farm. Disease reporting is essential to detect and respond to disease early, thereby minimising its impacts on agriculture businesses, the economy, and the environment. Trust has been identified as an important factor influencing farmers’ disease reporting intentions but has not been quantitatively investigated. We use an established model of trust—the Integrative Model of Organisational Trust (IMOT)—to conceptualise how trust influences disease reporting intentions. We also examine how social identity is related to trust and disease reporting. Australian plant, livestock, and aquaculture farmers (N = 41) completed an online questionnaire developed from existing validated measures and we also developed two new measures for disease reporting intentions. Trust in government positively and significantly predicted farmer intentions to report suspected disease outbreaks, explaining 26% of the variance. For every one-unit increase in trust, disease reporting intentions increased by over four times. Results also support the role of shared values and group membership as aspects of social identity that influence trust and disease reporting. These results highlight the importance of government decision-makers developing and maintaining trust with farmers to support early detection and response to emergency disease outbreaks.

Risk Perception and Preventive Behavior During the COVID-19 Pandemic : Testing the Effects of Government Trust and Information Behaviors

ABSTRACT Given the absence of COVID-19 treatments, the best way to control the spread of the virus is to break the chain of infection by increasing public participation in preventive behaviors recommended by health authorities. This study proposes a moderated mediation model of information behaviors (e.g. information seeking and information verification) and trust in government that explores the relationship between risk perception and preventive behaviors regarding COVID-19. Using a survey study in South Korea, we conducted the moderated mediation analysis with latent moderated structural equation modeling (LMS). We found serial mediation effects for risk perception, information behaviors, and preventive behaviors, as people both seek out information and verify that information before adopting preventive behaviors. Additionally, trust in government moderated information behaviors in the relationship between risk perception and preventive behaviors, suggesting that trust in government encourages people to adopt more preventive actions via information seeking and information verification. Further implications are discussed to promote public understanding of the health crisis and public participation in preventive measures.

The future of standardised assessment: Validity and trust in algorithms for assessment and scoring

AbstractThis article considers the challenges of using artificial intelligence (AI) and machine learning (ML) to assist high‐stakes standardised assessment. It focuses on the detrimental effect that even state‐of‐the‐art AI and ML systems could have on the validity of national exams of secondary education, and how lower validity would negatively affect trust in the system. To reach this conclusion, three unresolved issues in AI (unreliability, low explainability and bias) are addressed, to show how each of them would compromise the interpretations and uses of exam results (i.e., exam validity). Furthermore, the article relates validity to trust, and specifically to the ABI+ model of trust. Evidence gathered as part of exam validation supports each of the four trust‐enabling components of the ABI+ model (ability, benevolence, integrity and predictability). It is argued, therefore, that the three AI barriers to exam validity limit the extent to which an AI‐assisted exam system could be trusted. The article suggests that addressing the issues of AI unreliability, low explainability and bias should be sufficient to put AI‐assisted exams on par with traditional ones, but might not go as far as fully reassure the public. To achieve this, it is argued that changes to the quality assurance mechanisms of the exam system will be required. This may involve, for example, integrating principled AI frameworks in assessment policy and regulation.

RTM: Realistic Weight-Based Reliable Trust Model for Large Scale WSNs

RTM: Realistic Weight-Based Reliable Trust Model for Large Scale WSNs

A Survey on Trust Metrics for Autonomous Robotic Systems

This paper surveys the area of “Trust Metrics” related to security for autonomous robotic systems. As the robotics industry undergoes a transformation from programmed, task oriented, systems to Artificial Intelligence-enabled learning, these autonomous systems become vulnerable to several security risks, making a security assessment of these systems of critical importance. Therefore, our focus is on a holistic approach for assessing system trust which requires incorporating system, hardware, software, cognitive robustness, and supplier level trust metrics into a unified model of trust. We set out to determine if there were already trust metrics that defined such a holistic system approach. While there are extensive writings related to various aspects of robotic systems such as, risk management, safety, security assurance and so on, each source only covered subsets of an overall system and did not consistently incorporate the relevant costs in their metrics. This paper attempts to put this prior work into perspective, and to show how it might be extended to develop useful systemlevel trust metrics for evaluating complex robotic (and other) systems.

Trust Evaluation Model in IoT Environment: A Comprehensive Survey

The Internet of Things (IoT) is a rapidly evolving field that provides seamless connections to a physical object, making it part of a smart environment. To fully realize the potential power of the connections between these objects in IoT, trust between them is critical. Conventional security measures are not sufficient to provide comprehensive security for this smart world. Trust is used to reduce the risk of insecurity when nodes are connected to the Internet. In an IoT environment, various trust models have been proposed for Wireless Sensor Networks (WSNs) and Radio Frequency Identification (RFID). Nevertheless, these are not fully adapted to the dynamic and uncertain environment of the intelligent IoT. Therefore, this article reviews the characteristics of recent works in IoT trust models. A comprehensive study has been conducted on the classification of trust models. A set of factors are discussed, such as trust characteristics, trust architecture, trust distribution, trust aggregation technique, trust model type, and attack type. In addition, this paper provides readers with an understanding of the current existing trust model and directs future works to propose new models that satisfy all characteristics that should be considered when developing a trust model. Finally, some research challenges and directions are identified.

Certrust: An SDN-Based Framework for the Trust of Certificates against Crossfire Attacks in IoT Scenarios

The low-intensity attack flows used by Crossfire attacks are hard to distinguish from legitimate flows. Traditional methods to identify the malicious flows in Crossfire attacks are rerouting, which is based on statistics. In these existing mechanisms, the identification of malicious flows depends on the IP address. However, the IP address is easy to be changed by attacks. Compared with the IP address, the certificate is more challenging to be tampered with or forged. Moreover, the traffic trend in the network is towards encryption. The certificates are popularly utilized by IoT devices for authentication in encryption protocols. DTLShps proposed a new way to verify certificates for resource-constrained IoT devices by using the SDN controller. Based on DTLShps, the SDN controller can collect statistics on certificates. In this paper, we propose Certrust, a framework based on the trust of certificates, to mitigate the Crossfire attack by using SDN for IoT. Our goal is threefold. First, the trust model is built based on the Bayesian trust system with the statistics on the participation of certificates in each Crossfire attack. Moreover, the forgetting curve is utilized instead of the traditional decay method in the Bayesian trust system for achieving a moderate decay rate. Second, for detecting the Crossfire attack accurately, a method based on graph connectivity is proposed. Third, several trust-based routing principles are proposed to mitigate the Crossfire attack. These principles can also encourage users to use certificates in communication. The performance evaluation shows that Certrust is more effective in mitigating the Crossfire attack than the traditional rerouting schemes. Moreover, our trust model has a more appropriate decay rate than the traditional methods.

A Cross-Domain Trust Model of Smart City IoT Based on Self-Certification

Smart city refers to the information system with Internet of things and cloud computing as the core technology and government management and industrial development as the core content, forming a large-scale, heterogeneous and dynamic distributed Internet of things environment between different Internet of things. There is a wide demand for cooperation between equipment and management institutions in the smart city. Therefore, it is necessary to establish a trust mechanism to promote cooperation, and based on this, prevent data disorder caused by the interaction between honest terminals and malicious terminals. However, most of the existing research on trust mechanism is divorced from the Internet of things environment, and does not consider the characteristics of limited computing and storage capacity and large differences of Internet of things devices, resulting in the fact that the research on abstract trust mechanism cannot be directly applied to the Internet of things; On the other hand, various threats to the Internet of things caused by security vulnerabilities such as collision attacks are not considered. Aiming at the security problems of cross domain trusted authentication of Intelligent City Internet of things terminals, a cross domain trust model (CDTM) based on self-authentication is proposed. Unlike most trust models, this model uses self-certified trust. The cross-domain process of internet of things (IoT) terminal can quickly establish a trust relationship with the current domain by providing its trust certificate stored in the previous domain interaction. At the same time, in order to alleviate the collision attack and improve the accuracy of trust evaluation, the overall trust value is calculated by comprehensively considering the quantity weight, time attenuation weight and similarity weight. Finally, the simulation results show that CDTM has good anti collusion attack ability. The success rate of malicious interaction will not increase significantly. Compared with other models, the resource consumption of our proposed model is significantly reduced.

T-PORP: A Trusted Parallel Route Planning Model on Dynamic Road Networks

Route planning over dynamic road networks is an increasingly fundamental problem of modern transportation systems for human society, especially in the field of Intelligent Supply Chain (ISC). Due to the high degree of urbanization and the high number of vehicles, longer response time caused by massive concurrent queries, as well as more attacks caused by malicious vehicles, results in low efficiency of the transportation system and huge waste of computation resources. Thus, it is necessary to provide an efficient and safe transportation service for intelligent transportation planning. To achieve it, we utilize and improve the trust model to prevent the waste of computation resources. Meanwhile, we introduce a Trusted Parallel Optimization on Route Planning (T-PORP) based on Dual-level Grid (DLG) index to continuously handle the process of route planning in parallel. Considering the evolving traffic condition, we employ an LSTM (Long Short-Term Memory) neural network to periodically predict the weights of roads. Experimental results indicate that T-PORP is effective to sorts of trust model attacks and reduces the response time by an average of about 46.7% and saves the processing time by an average of about 27.6% compared with CANDS (Continuous Optimal Navigation via Distributed Stream Processing) algorithm.

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.

A Blockchain-Based Trust Model for Supporting Collaborative Healthcare Data Management

The development of information technology allows the collaborative business process to be run across multiple enterprises in a larger market environment. However, while collaborative business expands the realm of businesses, it also causes various hazards in collaborative Interaction, such as data falsification, inconstancy, and misuse. To solve these issues, a blockchain-based collaborative business modeling approach was proposed and analyzed. However, the existing studies lack the blockchain risk problem-solving specification, and there is no verification technique to examine the process. Consequently, it is difficult to confirm the appropriateness of the approach. Thus, here, we propose and build a blockchain-based trust model to strengthen and verify the integrity and security of the collaborative business process; Integrity and security address the validity of collaborative interactions in terms of a trust, and we construct a blockchain pattern based on trust elements to meet the required the characteristics. Specifically, a trust model can be applied to the healthcare data-sharing process, and then the achievement of the trust-based safe data-sharing process can be proven. Our model can be used as a trust-building guidance tool or for integrity and security verification with the collaborative business process in a distributed environment with blockchain.

Suitable Scalability Management Model for Software-Defined Perimeter Based on Zero-Trust Model

The software-defined perimeter (SDP), a zero-trust model developed by the Cloud Security Alliance, has been attracting attention in the technological industry since its introduction to a world adapting to digital transformation. Many trust models have been introduced to meet the growing demands for cyber security, such as the public key infrastructure, soft-ware-defined network, and virtual private network. SDP has particularly gained importance as a zero-trust model because no one in the digital world can be trusted. With the introduction of new models and technical devices, there is now a need to improve newly introduced technology on various grounds when customers adapt to devices. In this work, we discuss how to overcome the current issues of SDP relating to scalability, reliability, usability, etc. As the number of organizations that share information online continues to expand, there is a need for scalable and reliable SDP models that are both easy to maintain and cost efficient for evolving organizations. To meet this need, we proposed several scalable SDP models that enable easier installation management of real networks of organizations with different organizational structures. Specifically, we propose hierarchical, bridge, hybrid, and mesh models. The results of qualitative and quantitative evaluations showed that the bridge model is the most suitable of the four as an extension of SDP.

A Survey on Microservices Trust Models for Open Systems

The microservices architecture (MSA) is a form of distributed systems architecture that has been widely adopted in large-scale software systems in recent years. As with other distributed system architectures, one of the challenges that MSA faces is establishing trust between the microservices, particularly in the context of open systems. The boundaries of open systems are unlimited and unknown, which means they can be applied to any use case. Microservices can leave or join an open system arbitrarily, without restriction as to ownership or origin, and scale extensively. The organisation of microservices (in terms of the roles they play and the communication links they utilise) can also change in response to changes in the environment that the system is situated in. The management of trust within MSAs is of great importance as the concept of trust is critical to microservices communication, and the operation of an open MSA system is highly reliant on communication between these fine-grained microservices. Thus, a trust model should also be able to manage trust in an open environment. Current trust management solutions, however, are often domain-specific and many are not specifically tailored towards the open system model. This motivates research on trust management in the context of open MSA systems. In this paper, we examine existing microservices trust models, identify the limitations of these models in the context of the principles of open microservices systems, propose a set of qualities for open microservices trust models that emerge from these limitations, and assess selected microservices trust models using the proposed qualities.

Enhanced Energy Efficient with a Trust Aware in MANET for Real-Time Applications

Mobile ad hoc networks (MANETs) are subjected to attack detection for transmitting and creating new messages or existing message modifications. The attacker on another node evaluates the forging activity in the message directly or indirectly. Every node sends short packets in a MANET environment with its identifier, location on the map, and time through beacons. The attackers on the network broadcast the warning message using faked coordinates, providing the appearance of a network collision. Similarly, MANET degrades the channel utilization performance. Performance highly affects network performance through security algorithms. This paper developed a trust management technique called Enhanced Beacon Trust Management with Hybrid Optimization (EBTM-Hyopt) for efficient cluster head selection and malicious node detection. It tries to build trust among connected nodes and may improve security by requiring every participating node to develop and distribute genuine, accurate, and trustworthy material across the network. Specifically, optimized cluster head election is done periodically to reduce and balance the energy consumption to improve the lifetime network. The cluster head election optimization is based on hybridizing Particle Swarm Optimization (PSO) and Gravitational Search Optimization Algorithm (GSOA) concepts to enable and ensure reliable routing. Simulation results show that the proposed EBTM-HYOPT outperforms the state-of-the-art trust model in terms of 297.99 kbps of throughput, 46.34% of PDR, 13% of energy consumption, 165.6 kbps of packet loss, 67.49% of end-to-end delay, and 16.34% of packet length.

A trust management model in internet of vehicles

The Internet of Things (IoT) is one of the most evolving technologies, which has a major impact on our daily life. Almost all new devices will have a feature to be connected and controlled over the Internet. Several applications are utilizing IoT to enhance routine processes and actions efficiently. The Internet of Vehicles (IoV) evolved from IoT, where vehicles communicate with each other or with other objects to have a better transportation environment to reduce the number of accidents and save people’s lives. IoV is considered new fields that need security requirements including confidentiality, integrity, availability, authentication, and trust. Trust management technique is used to validate entities behaviors automatically against well-defined policies. The major categories of trust model in IoV are based on entity, data, or a combination of both. This paper proposes a trust model which is based on a combination of entity and data to define the trust of vehicles and utilize the public key infrastructure to distribute certificates to vehicles. Based on certificate validation, messages will be trusted and accepted. This model has been tested across different simulation scenarios which showed that the proposed model detected malicious vehicles and trusted vehicles did not accept their messages.

Modification of the reputation and trust model in the tasks of information security of grid systems for resistance to the threat of "malicious host groups"

In Grid-systems, the key idea is the joint use of resources, therefore there is a need for mutual trust between users and resource providers. In small grid systems, all participants are in a relationship of complete trust. For example, in the Ukrainian Academic Grid segment, all participants belong to the National Academy of Sciences of Ukraine, and on this basis there is complete trust. But in larger Grid-systems, participants may often not be directly connected to each other, and there is a risk that one of the participants will turn out to be unscrupulous and malicious. Trust mechanisms are designed to reduce these risks. The main purpose of security mechanisms is to provide protection against malicious users and groups of persons. Traditional security methods usually protect resources from malicious influences by restricting access to authorized users. However, in most cases there is a need to protect the system and its components from those who provide resources and services within heterogeneous computing systems. Thus, there are a large number of problems in the field of security that cannot be solved within the framework of traditional approaches. Information providers can, for example, commit fraud by providing false and unreliable information, and traditional security mechanisms are unable to protect against this type of threat. On the other hand, systems of reputation and trust can provide protection against these risks. The distinction between these two approaches to information security was first described by Rasmussen and Jansson (1996), who used the term hard security to refer to traditional mechanisms such as authentication and access control, and the term soft security to refer to what they called public control mechanisms. in general, exemplified by reputation and trust systems.

Approaches to Overpower Proof-of-Work Blockchains Despite Minority

Blockchain (BC) technology has been established in 2009 by Nakamoto, using the Proof-of-Work (PoW) to reach consensus in public permissionless networks (Praveen et al., 2020). Since then, several consensus algorithms were proposed to provide equal (or higher) levels of security, democracy, and scalability, yet with lower levels of energy consumption. However, Nakamoto’s model (a.k.a. Bitcoin) still dominates as the most trusted model in the described sittings since alternative solutions might provide lower energy consumption and higher scalability, but they would always require deviating the system towards unrecommended centralization or lower levels of security. That is, Nakamoto’s model claims to tolerate (up to) < 50% of the network being controlled by a dishonest party (minority), which cannot be realized in alternative solutions without sacrificing the full decentralization property. In this paper, we investigate this tolerance claim, and we review several approaches that can be used to undermine/overpower PoW-based BCs, even with minority. We discuss those BCs taking Bitcoin as a representative application, where needed. However, the presented approaches can be applied in any PoW-based BC. Specifically, we technically discuss how a dishonest miner in minority, can take over the network using improved Brute-forcing, AI-assisted mining, Quantum Computing, Sharding, Partial Pre-imaging, Selfish mining, among other approaches. Our review serves as a needed collective technical reference (concluding more than 100 references), for practitioners and researchers, who either seek a reliable security implementation of PoW-based BC applications, or seek a comparison of PoW-based, against other BCs, in terms of adversary tolerance.