Trust Evaluation Scheme Research Articles

A trustworthy data collection approach from sensor nodes using trust score of mobile vehicles for smart city

AbstractIn smart cities, a substantial amount of data is collected for analytics and a better life for the citizens. The schemes based on data collection through mobile vehicles (MV) and further verification of that data through unmanned aerial vehicles (UAV) are popular. Many trust‐based schemes of the MV have been proposed recently. However, these schemes suffered from recognition accuracy, judgment trust, and collusion attack problems. In this paper, we propose a Gompetz function‐based trust evaluation scheme. In this scheme, the direct trust of the MV is computed by comparing the data provided by the MV and the same reported by the UAV. Since the UAV can collect only limited data, indirect trust of the vehicle is computed by comparing the data reported by the MV and the same reported by the MV having the highest trust. We also applied the variable trust, which considers the recent Trust of the MVs. Then, combining all these trusts with significant weight, the final trust score of the MV is computed. After experimenting, our proposed scheme is more credible and removes the shortcomings of the existing methods by providing better recognition, accuracy, judgment, and trust.

TFL-DT: A Trust Evaluation Scheme for Federated Learning in Digital Twin for Mobile Networks

TFL-DT: A Trust Evaluation Scheme for Federated Learning in Digital Twin for Mobile Networks

Access Control Strategy for the Internet of Vehicles Based on Blockchain and Edge Computing

Data stored in the Internet of Vehicles (IoV) face problems with ease of tampering, easy disclosure and single access control. Based on this problem, we propose an access control scheme for the IoV based on blockchain, trust values and weighted attribute-based encryption, called the Blockchain Trust and Weighted Attribute-Based Access Control Strategy (BTWACS). First, we utilize both local and global blockchains to jointly maintain the generation, verification and storage of blocks, achieving distributed data storage and ensuring that data cannot arbitrarily be tampered with. Local blockchain mainly uses Road Side Unit (RSU) technology to calculate trust values, while global blockchain is mainly responsible for data storage and access policy selection. Secondly, we design a blockchain-based trust evaluation scheme called Blockchain-Based Trust Evaluation (BBTE). In this evaluation scheme, the trust value of the vehicle node is based on four factors: initial trust, historical experience trust, recommendation trust and RSU observation trust. CRITIC is used to determine the optimal weights of four factors to obtain the trust value. Then, we use the Network Simulator version 3 (NS3) to verify the security and accuracy of BBTE, improving the recognition accuracy and detection rate of malicious vehicle nodes. Finally, by mining the association relationships between attribute permissions among various roles, we construct a hierarchical access control strategy based on weight and trust, and further optimize the access strategy through pruning techniques. The experiment results indicate that this scheme can effectively respond to gray hole attacks, defamation attacks and collusion attacks from other vehicle nodes. This method can effectively reduce the computing and transmission costs of vehicles and meet the access requirements of multiple entities and roles in the IoV.

Trust in Vehicles: Toward Context-Aware Trust and Attack Resistance for the Internet of Vehicles

Trust evaluation and management schemes have been extensively employed in a bid to alleviate diverse insider attacks. These trust values are often ascertained by taking into consideration trust parameters to determine the honesty and reliability of a vehicle and evaluating a weighted sum of the said parameters. To achieve precision and to reflect a reasonable impact of these parameters, rational weight values are imperative. Accordingly, this research primarily emphasizes on the quantification of weights associated with the contributing trust attributes by proposing a novel trust management mechanism that utilizes contextual information in addition to employing relevant impacting quantities as weights to formulate trust evaluations. Moreover, the envisaged trust management model incorporates 1) attack resilience while constituting certain parameters and 2) an adaptive and flexible threshold to mitigate malevolent behaviors. The simulation results depict that the devised parameters and the formulated trust aggregation cater to the dynamic nature of vehicular networks demonstrating the rationality of the weights’ quantification, and the introduction of the adaptive threshold for misbehavior detection aligns well with the requirements of the ever-changing vehicular networks.

CITE: A content based trust evaluation scheme for data collection with Internet of Everything

Sensor–cloud system, as one of the vital components of the Internet of Everything, has increasingly gained popularity since it bridges the physical and cyber worlds. In the smart city, sensing devices are deployed to monitor the environment and the data aggregate at the roadside Aggregation Devices (ADs). Then, Mobile Vehicles (MVs) submit the data to the data center in an opportunistic routing way when passing ADs. We propose a Content based Intelligent Trust Evaluation (CITE) scheme for trustworthy data collection over large-scale sensor-cloud systems. In CITE scheme, unmanned Mobile Electric Vehicles (MEVs) are employed to visit some sensing devices and collect their data along the designed routing path. Then, a delicate comparison algorithm is proposed to determine the credibility of devices' data operations, and trust evaluation is based on it. Similarly, the trust of MVs is obtained by comparing the data reported from ADs with the data submitted to the data center. Moreover, to reduce the cost of MEVs, we also design a path planning algorithm. The simulation results show that the CITE scheme can identify reliable sensing devices and MVs with a rate of 87.3% and 100% and increase the collection rate by 25.2% on average compared with the previous strategies.

An efficient trust-based decision-making approach for WSNs: Machine learning oriented approach

Wireless Sensor Networks (WSNs) are often used for critical applications where trust and security are of paramount importance. Trust evaluation is one of the key mechanisms to ensure the security and reliability of WSNs. Traditional trust evaluation schemes rely on fixed, predetermined thresholds, or rules and static attack models, which may not be suitable for all situations such as dynamic and heterogeneous network environments with new and unknown attack scenarios as well as have several problems such as limited security and scalability, limited accuracy, incomplete coverage, lack of adaptability that can limit their effectiveness. Machine Learning (ML) has been shown to be an effective tool for trust evaluation in WSNs, offering several benefits over existing schemes such as greater adaptability, scalability, and accuracy since ML algorithms can analyze and learn from the data collected in real-time from multiple sources (sensor readings, network traffic, and user behavior) enabling them to dynamically adjust their decision-making criteria based on the current network conditions. Trust-aware ML-based security mechanisms achieve safety and efficient decision-making by reducing uncertainty and risk to accomplish real-world tasks. This paper presents a Machine Learning (ML)-based trust evaluation model in the unattended autonomous WSN environment to achieve reliability, adaptability, scalability, and accuracy by generating quick and reliable trust values dynamically. The proposed machine learning algorithm extracts various trust features such as Co-Location Relationship (CLR), Co-Work Relationship (CWR), Cooperativeness-Frequency-Duration (CFD), and Reward (R) to obtain a robust trust rating of sensor devices and predict future misbehavior. These trust features are combined to generate a final trust rating before making any decision about the reliability of any sensor device. Moreover, the projected trust model (ETDMA) integrate direct communication trust and indirect trust with the help of a logical time window that periodically records the trustworthy and suspicious interactions. Simulation experiments exhibit the effectiveness of the proposed trust evaluation method in terms of change in trust values, malicious nodes detection (94%), FNR (0.9%), F1-Score (0.6), and accuracy (92%) in the presence of 50 malicious nodes.

A Fuzzy-Based Co-Incentive Trust Evaluation Scheme for Edge Computing in CEEC Environment

With the development of 5G and artificial intelligence, the security of Cloud-Edge-End Collaboration (CEEC) networks becomes an increasingly prominent issue due to the complexity of the environment, real-time variability and diversity of edge devices in CEEC networks. In this paper, we design a lightweight fuzzy collaborative trust evaluation model (LFCTEM) for edge devices, and calculate the trust values of edge devices by fuzzifying trust factors. To alleviate the selfish behavior of edge devices, this paper introduces an incentive mechanism in the trust evaluation model, and achieves a long-term incentive effect by designing an incentive negative decay mechanism, which enhances the initiative of collaboration and improves the interference resistance of CEEC networks. We verify the performance of LFCTEM through simulation experiments. Compared with other methods, our model enhances the detection rate of malicious edge devices by 19.11%, which improves the reliability of the CEEC trust environment. Meanwhile, our model reduces the error detection rate of edge devices by 16.20%, thus alleviating error reporting of the CEEC trust environment.

Entity and Sociality Trust-Aware Model for Content Distribution in Social Internet of Vehicles

Social Internet of Vehicles (SIoV) introduces human social behaviors, which is not completely trusted and may be accompanied by malicious information. Entity trust, the communication capability of equipment, and sociality trust, the social status of humans, are factors affecting the content distribution in SIoV. The entity is the carrier of content distribution, while sociality affects the stability of content distribution and network security. However, the existing trust evaluation schemes didn't consider the balance between entity and sociality, which affects the SIoV's quality of service (QoS). In this paper, we propose an entity and sociality trust-aware model for content distribution in SIoV. The model makes a trade-off between entity trust and sociality trust (social activity and the feedback of acquaintances) based on the fuzzy comprehensive evaluation, to obtain an overall trust level and limit the driving application functions and social behavior in SIoV. In addition, the blocking rules of content distribution are improved, taking into account trust value and location, including the extreme situation of the proportion of untrusted vehicles in SIoV. The experiment results of both simulation and the real road layout in Madrid, Spain show that the model effectively improves the security of the network and QoS in terms of content reception and delivery time.

Trust-Aware Routing Mechanism through an Edge Node for IoT-Enabled Sensor Networks.

Although IoT technology is advanced, wireless systems are prone to faults and attacks. The replaying information about routing in the case of multi-hop routing has led to the problem of identity deception among nodes. The devastating attacks against the routing protocols as well as harsh network conditions make the situation even worse. Although most of the research in the literature aim at making the IoT system more trustworthy and ensuring faultlessness, it is still a challenging task. Motivated by this, the present proposal introduces a trust-aware routing mechanism (TARM), which uses an edge node with mobility feature that can collect data from faultless nodes. The edge node works based on a trust evaluation method, which segregates the faulty and anomalous nodes from normal nodes. In TARM, a modified gray wolf optimization (GWO) is used for forming the clusters out of the deployed sensor nodes. Once the clusters are formed, each cluster’s trust values are calculated, and the edge node starts collecting data only from trustworthy nodes via the respective cluster heads. The artificial bee colony optimization algorithm executes the optimal routing path from the trustworthy nodes to the mobile edge node. The simulations show that the proposed method exhibits around a 58% hike in trustworthiness, ensuring the high security offered by the proposed trust evaluation scheme when validated with other similar approaches. It also shows a detection rate of 96.7% in detecting untrustworthy nodes. Additionally, the accuracy of the proposed method reaches 91.96%, which is recorded to be the highest among the similar latest schemes. The performance of the proposed approach has proved that it has overcome many weaknesses of previous similar techniques with low cost and mitigated complexity.

A node trust evaluation method of vehicle-road-cloud collaborative system based on federated learning

As the vehicle-road-cloud collaboration system develops rapidly, it is accompanied by serious information security problems while solving the data transmission issues. For constructing secure transmission of data, trust is recommended as a relevant way to accomplish network security; that is, developing a trust model that can be used by sensor nodes to determine the reliability of another node is crucial. However, the heterogeneity of the network has different functional requirements for trust evaluation, and the openness of the network makes the nodes more vulnerable to attacks. Therefore, the research of trust evaluation model in the vehicle-road-cloud collaborative system is facing greater challenges than the traditional network. In this paper, a trust evaluation scheme of the vehicle-road-cloud collaborative system based on Federated Learning (FLT) is proposed. A hierarchical trust evaluation model is designed, and the complex model is simplified to an orderly hierarchical structure by using hierarchical analysis. The trust indexes of different layers are evaluated, and the influencing factors among different nodes are comprehensively considered. Combined with federated learning, it solves the problem of finding the most reliable route and realizes personalization at the level of equipment, data, and model. For the purpose of alleviating the heterogeneity and obtaining a high-quality personalized model for each device, trust values can be adaptively updated as changes in the topology of the network occur in real-time. The simulation findings demonstrate that, as compared to previous schemes, the energy consumption is lowered by 35%, and the accuracy is raised by 45% while maintaining trust stability.

An Intelligent and Trust UAV-Assisted Code Dissemination 5G System for Industrial Internet-of-Things

A large number of devices with communication and sensing capabilities are connected to the 5G network and facilitate distributed industrial Internet of Things (IIoT) applications. Those devices can update their program code by using software-defined technologies and upgrade functions without hardware replacement, thus, greatly facilitating the development of IIoT applications. Disseminating code safely to these devices is a pivotal issue. However, the existing methods rarely consider whether the code received by devices is integrated. Therefore, an unmanned aerial vehicle (UAV) assisted trustworthy code dissemination (UTCD) framework in 5G-enabled intelligent IIoT systems is proposed to select credible mobile vehicles (MVs) to disseminate code with opportunistic routing style. In the UTCD framework, a verifiable trust evaluation scheme is proposed to identify the trust of the MVs by sending the UAV to collect the code wait to be verified (CWV) directly from the selected devices (bedrock devices) as evidence. This scheme is a fundamental change from the previous passive, indirect and unverifiable trust evaluation schemes. In UTCD, a bedrock devices selection scheme is presented by selecting as few bedrock devices as possible to collect as much the CWV as possible with the minimum cost. After that, based on the CWV, which is collected from bedrock devices, a complete trust evaluation scheme is proposed to obtain the trust of MVs. Furthermore, a UAV trajectory optimization algorithm is proposed to obtain as much CWV as possible within the limited conditions of UAV. Finally, comprehensive experiments conduct on a real-life dataset demonstrate that the proposed scheme outperforms the existing schemes in terms of the efficiency and security of code dissemination.

A trust evaluation scheme of service providers in mobile edge computing

Mobile edge computing (MEC) is a new computing paradigm that brings cloud services to the network edge. Despite its great need in terms of computational services in daily life, service users may have several concerns while selecting a suitable service provider to fulfil their computational requirements. Such concerns are: with whom they are dealing with, where will their private data migrate to, service provider processing performance quality. Therefore, this paper presents a trust evaluation scheme that evaluates the processing performance of a service provider in the MEC environment. Processing performance of service providers is evaluated in terms of average processing success rate and processing throughput, thus allocating a service provider in a relevant trust status. Service provider processing incompliance and user termination ratio are also computed during provider’s interactions with users. This is in an attempt to help future service users to be acknowledged of service provider’s past interactions prior dealing with it. Thus, eliminating the probability of existing compromised service providers and raising the security and success of future interactions between service providers and users. Simulations results show service providers processing performance degree, processing incompliance and user termination ratio. A service provider is allocated to a trust status according to the evaluated processing performance trust degree.

A fuzzy-based trust evaluation framework for efficient privacy preservation and secure authentication in VANET

ABSTRACT The Vehicular Adhoc Network (VANET) is a newly added smart technique in vehicles to ensure safety and reduce time consumption. Even though it saves time and guarantees safe travel, security and privacy are the most difficult issues in the VANET. Moreover, this is due to the fact that the methods exploit public key infrastructure, group signature, etc., Meanwhile, the hackers can acquire the sensitive data’s which are usually kept in the tamper-proof devices by using side-channel attacks. The VANET also possesses several security-related issues. To circumvent this we propose an efficient privacy-preserving and fuzzy-based trust evaluation scheme. This method ensures the security and authenticity of the VANET. To ensure security our proposed method utilizes a modified Elliptical Curve cryptographic (ECC) method which also reduces the computational complexities created by the conventional ECC. In our proposed method the TPD parameters are renewed more often to eliminate the attacks and permits batch verification methods to reduce the time. The experimental analysis is conducted in Matlab simulator in terms of computational cost, communication cost, evaluation of trustworthiness, privacy protection. The experimental analysis shows that proposed method provides 94% of trustworthiness and time consumption and communication overheads are reduced to greater extent.

A Trust-Driven Contract Incentive Scheme for Mobile Crowd-Sensing Networks

By leveraging the power of crowd, the prevalence of mobile devices in mobile crowd-sensing (MCS) networks helps and provides a wide range of sensing services through collecting and sharing sensing data. However, due to the diverse behaviours of mobile users, unreliable or malicious users and platforms could provide untrusted data, which affects the quality of sensing service. Besides, mobile users are reluctant to participate in sensing tasks without sufficient incentives. It is desirable to design a trust and incentive scheme to improve the service efficiency of MCS. In this paper, we propose a novel trust-driven contract incentive framework in MCS, which guarantees the service quality and stimulates mobile users to join sensing tasks. We first design a trust evaluation scheme between mobile users and sensing platforms based on the historical interactions to derive the reliability value of sensing platform. Then, the trust threshold is formulated to filter out malicious sensing platforms. By considering the privacy preferences of mobile users, we establish a contract incentive scheme to maximize the utility of both mobile users and sensing platforms. The design objective is to derive a set of optimal contracts under both discrete and continuous contract models. Meanwhile, the designed contracts guarantee the individual rationality (IR) and incentive compatibility (IC) properties. Finally, simulations are conducted to evaluate the effectiveness of the proposed trust-driven contract incentive scheme, and results demonstrate that the proposed scheme can jointly improve the quality of sensing service and maximize the utilities of mobile users and sensing platforms.

LPPTE: A lightweight privacy-preserving trust evaluation scheme for facilitating distributed data fusion in cooperative vehicular safety applications

Vehicular networks have tremendous potential to improve road safety, traffic efficiency, and driving comfort, where cooperative vehicular safety applications are a significant branch. In cooperative vehicular safety applications, through the distributed data fusion for large amounts of data from multiple nearby vehicles, each vehicle can intelligently perceive the surrounding conditions beyond the capability of its own onboard sensors. Trust evaluation and privacy preservation are two primary concerns for facilitating the distributed data fusion in cooperative vehicular safety applications. They have conflicting requirements and a good balance between them is urgently needed. Meanwhile, the computation, communication, and storage overheads will all influence the applicability of a candidate scheme. In this paper, we propose a Lightweight Privacy-Preserving Trust Evaluation (LPPTE) scheme which can primely balance the trust evaluation and privacy preservation with low overheads for facilitating the distributed data fusion in cooperative vehicular safety applications. Furthermore, we provide exhaustive theoretical analysis and simulation evaluation for the LPPTE scheme, and the results demonstrate that the LPPTE scheme can obviously improve the accuracy of fusion results and is significantly superior to the state-of-the-art schemes in multiple aspects.

Social-Chain

Pervasive Social Networking (PSN) supports online and instant social activities with the support of heterogeneous networks. Since reciprocal activities among both familiar/unfamiliar strangers and acquaintances are quite common in PSN, it is essential to offer trust information to PSN users. Past work normally evaluates trust based on a centralized party, which is not feasible due to the dynamic changes of PSN topology and its specific characteristics. The literature still lacks a decentralized trust evaluation scheme in PSN. In this article, we propose a novel blockchain-based decentralized system for trust evaluation in PSN, called Social-Chain. Considering mobile devices normally lack computing resources to process cryptographic puzzle calculation, we design a lightweight consensus mechanism based on Proof-of-Trust (PoT), which remarkably improves system effectivity compared with other blockchain systems. Serious security analysis and experimental results further illustrate the security and efficiency of Social-Chain for being feasibly applied into PSN.

A Reliable Lightweight Trust Evaluation Scheme for IoT Security

The rapid development of smart devices and the consequent demand their reliability have posed many challenges limiting their versatility. One of the most significant challenges is safeguarding the widespread network of sensors and devices within harsh remote environments. Numerous trust schemes have been proposed to overcome related IoT security concerns. How-ever, most of these schemes are not lightweight and consequently are not energy-efficient. This paper proposes a reliable lightweight trust evaluation scheme (RTE) to mitigate the malicious behavior of the nodes within IoT networks. The nodes are grouped into a set of clusters each having a cluster head while cluster members are categorized by evaluating their associated residual energy. Nodes with residual energy lower than the threshold (which is determined by the base station) are suspended until they recover and regain their activity. The computations are handled by the CH which is elected by an algorithm according to its energy and coverage degree in order to optimize the energy consumption in the network. For validation and performance evaluation, the proposed RTE scheme was compared to three of the recent schemes in its category. The obtained results have revealed that the proposed RTE scheme outperforms all of them in terms of detection rate, trust evaluation time, and energy efficiency.

A Trust Based Scheme to Protect 5G UAV Communication Networks

With the increasing demands of social services, Unmanned Aerial Vehicles (UAVs)-assisted networks promote promising prospect for improving social services. The sensing data can be collected by UAVs, a large number of applications based on UAVs can be realized in 5G networks. However, the malicious UAVs may provide false information and destroy services. The 5G UAV communication systems face the security threats. Therefore, this paper develops a novel trust-based security scheme for 5G UAV communication systems. The architecture of 5G UAV communication system is first presented to improve the communication performance. Second, a trust evaluation scheme for UAVs is developed to evaluate the reliability of UAVs. By introducing the trust threshold, the malicious UAVs will be filtered out from the systems. Third, with the assistance of mobile edge computing (MEC) nodes, the detection scheme based on Q-learning technology is proposed to detect the malicious attacks. The security threats can be detected to protect the security of systems. Finally, simulation results demonstrate the effectiveness of the proposed scheme.

Continuous Trust Evaluation of Power Equipment and Users Based on Risk Measurement

In power Internet of Things environment, the existing border-based protection system and the “one-time authentication, one-time authorization, and long-term effective” approach are difficult to deal with the threat of attacks from internal and external devices and users with legal authority. In order to solve the problem of authorized access of power equipment and users, combined with behavior risk assessment, a continuous trust evaluation scheme of power equipment and users is presented in this paper. The scheme is evaluated by the combination of direct trust, indirect trust, and comprehensive trust and adds the penalty reward factor and time attenuation function to improve the reliability of the results. In addition, this paper will quantify the risk of the behavior of power equipment and users and regard it as a factor affecting the degree of trust, so as to achieve continuous trust evaluation of equipment and users.

Blockchain and AI Empowered Trust-Information-Centric Network for Beyond 5G

As the next-generation network, beyond fifth generation (B5G) provides transmission capability up to terabits and processes hundreds of exabytes of content data per day from the internet of Everything. From 5G to B5G, the information-centric network (iCN) is expected to play a vital role due to the strong capabilities of content distribution, caching, and processing. As security is a major concern in B5G, content trust of iCN is of critical importance. Lack of content trust leads to the untrustworthiness and maliciousness of services and applications in B5G, such as malicious accidents resulting from the untrusted content of vehicle navigation and autonomous system. To deal with this issue, we propose a blockchain and artificial intelligence (Ai) empowered trust-information- centric network architecture for B5G. First, we design a blockchain-based trust evaluation and circulation scheme for B5G nodes called TrustCoin, which quantifies the credibility of B5G nodes in a dynamic and fine-grained way, and manages trust quotas of B5G nodes as well as trust-coin circulation. Second, to obtain the content credibility, we devise a credibility decision method based on content status and B5G nodes' behaviors by exploiting the excellent properties of deep reinforcement learning, which provides the intelligent allocation criterion for TrustCoin. Third, we propose a smart incentive mechanism for the endogenous trust of B5G networks according to the allocation criterion, thereby establishing the trust-information-centric network. Experimental results have verified the effectiveness of our proposed mechanism.