Trusted Computing Research Articles

Model checking combined trust and commitments in Multi-Agent Systems

Trust and social commitments have been studied with different objectives for communication in Multi-Agent Systems (MASs) separately. The purpose of this paper is to present the first logical framework to explore the relationship between two key concepts in autonomous MASs, namely trust and commitments among agents, along with its model checking algorithms. An analysis is performed on this relationship, with a specific emphasis on the perspectives of semantics and model checking. The analysis is carried out within the framework of Trust Computation Tree Logic with Commitments (CTLC) logic, an extended logic built on top of the standard branching temporal Computation Tree Logic (CTL) by including modalities for reasoning about commitments and their fulfillment. Moreover, the study employs a trust operator to facilitate trust reasoning. We propose thus Trust Computation Tree Logic with Commitments (TCTLC), a new logic able to express properties about trust and commitments simultaneously. Such rich properties cannot be expressed in other languages. We introduce the semantics using the extended formalism of interpreted systems. We propose some postulates and proofs that show how we can reason about combined trust and commitments in the logic proposed. New Binary Decision Diagram (BDD)-based algorithms for our logic are presented and implemented as additional libraries of the Model Checker for Multi-Agent Systems (MCMAS), the default model checker of MASs. Furthermore, we prove that although TCTLC extends CTL, its model checking algorithm remains polynomial (P-complete) with respect to the size of the model and the length of the formula. Similarly, the space complexity for concurrent programs also remains unchanged, which is polynomial (PSPACE-complete) with respect to the size of the components within these programs. Finally, to evaluate the proposed technique, we report the experimental results using an industrial case study and compare the results with those obtained on relevant benchmarks.

FUBA: A fuzzy‐based unmanned aerial vehicle behaviour analytics for trust management in flying ad‐hoc networks

AbstractFlying Ad‐Hoc Network (FANET) is a promising ad hoc networking paradigm that can offer new added value services in military and civilian applications. Typically, it incorporates a group of Unmanned Aerial Vehicles (UAVs), known as drones that collaborate and cooperate to accomplish several missions without human intervention. However, UAV communications are prone to various attacks and detecting malicious nodes is essential for efficient FANET operation. Trust management is an effective method that plays a significant role in the prediction and recognition of intrusions in FANETs. Specifically, evaluating node behaviour remains an important issue in this domain. For this purpose, the authors suggest using fuzzy logic, one of the most commonly used methods for trust computation, which classifies nodes based on multiple criteria to handle complex environments. In addition, the Received Signal Strength Indication (RSSI) is an important parameter that can be used in fuzzy logic to evaluate a drone's behaviour. However, in outdoor flying networks, the RSSI can be seriously influenced by the humidity of the air, which can dramatically impact the accuracy of the trust results. FUBA, a fuzzy‐based UAV behaviour analytics is presented for trust management in FANETs. By considering humidity as a new parameter, FUBA can identify insider threats and increase the overall network's trustworthiness under bad weather conditions. It is capable of performing well in outdoor flying networks. The simulation results indicate that the proposed model significantly outperforms FNDN and UNION in terms of the average end‐to‐end delay and the false positive ratio.

A novel multi-level clustering mechanism using heuristic approach for secure data transmission in WSN sector with various trust computation

Several sensor nodes are used in Wireless Sensor Networks (WSNs). A multi-level clustering-based multi-trust model is introduced in WSN. The model’s main intent is to compute trust value for performing secure transmission. Initially, to verify vulnerability, the watchdog counter provides the required trust output. Further, this is intended to build a multi-level trust clustering process. Here, multi-trust is carried out by energy trust, communication trust, and data trust. Hence, multi-trust is compared with a threshold value. Once the trust value is generated, it is given for processing the cluster groups. Due to more multi-trusting, it creates multi-level clustering for security level enhancement. After the cluster group is formed, the major aspect of CH is optimally obtained with a Modified Exploration-based Pelican Optimization Algorithm (ME-POA). Finally, performance is carried out in multi-objective functions, where parameters are defined as distance, delay, energy, and multi-trust. Thus, with the determination of optimal results, the proposed multi-level clustering proves that it offers secure data transmission over WSN.

A Design of Improved Trust-based Clustering Approach to Enhance the Energy Computation in Secured Internet of Things (IoT)

The Internet of Things (IoT) is the trending area that occupies maximum of the applications of intelligent communication. The nodes in the IoT system are located in the monitoring environment to attain stable and reliable communication. In between the nodes and the sink, similar data is sensed and transmitted at maximum of the period and that leads to high energy consumption. On the other hand, it increases the security issues of the network. So the main objective is reducing power utilization and increasing the IoT network security. In this paper, we suggest an improved trust-based clustering approach to enhance energy computation and security (ITCES-IoT) in IoT networks which can able to reduce energy consumption and increase the security among the nodes in the network. We first introduced an effective system model that can able to reduce the transmitting and receiving power during communication. Secondly, the initial cluster setup phase, LEACH-based cluster head (CH) selection phase, and re-clustering principles are discussed. Then, the trust calculation is established for both the cluster members (CM) and the CH through direct and indirect trust calculation processes followed by the best solution of path selection is performed between the CM and the sink node. We constructed Bad mouthing attacks, Ballot stuffing attacks, and detection mechanisms with a Fast Entropy algorithm. Finally, intra-cluster and inter-cluster communication are elaborated in the communication phase. Through this process, the nodes and CHs can able to perform effective communication with minimum power utilization, and the nodes with the transmitted information are secured from the external environment. At the end stage, we validate our proposed ITCES-IoT approach in certain scenarios in network simulator NS2 and their performance analysis includes the parameters of packet delivery ratio, network throughput, and packet loss and energy consumption. Then the final outcomes are compared with the base methods such as ECET-IoT, DRTP-IoT, and RCDA-IoT. From the results it is understood that the proposed ITCES-IoT outperforms the baseline methods in terms of delivery ratio and throughput and as well it consumes less power at the time of data transmission.

Privacy-preserving trust management method based on blockchain for cross-domain industrial IoT

The Industrial Internet of Things (IIOT) contains many devices from different autonomous domains (e.g., factories), which need to cooperate to complete complex manufacturing process. Devices from different domains collaborate with each other, which greatly raises trust concerns about device-to-device interactions. Existing trust management approaches may result in the risk of privacy leakage and low accuracy of trust evaluation. Thus, trust issues during interaction remain unsolved but imperative. In this paper, we propose a blockchain-based privacy-preserving trust management architecture PPTMA. Specifically, PPTMA adopts federated learning to train a task-specific trust model for different collaborative task. Our work is the first attempt to research the relationship between the weight calculation of trust metric and the change of context in the field of trust management. To preserve the privacy of devices, differential privacy (DP) is exploited during the trust evaluation process. In addition, a game theory-based incentive mechanism is proposed to encourage the IIOT device for actively and honestly submitting the trust data into the blockchain as so to promote the accuracy of trust computing. Finally, we also design a parallel consensus protocol (OPBFT) which realizes an assembly line to speed up the efficiency of the consensus process. The idea of consensus assembly line firstly proposed by us brings new opportunities for improving the consensus efficiency. Extensive experiments have been conducted to show the effectiveness and efficiency of the proposed method.

A Study on Data Protection in Cloud Environment

Data protection in the online environment pertains to the safeguarding of sensitive or important data kept, analyzed, or sent in cloud-based systems. It entails assuring data confidentiality, integrity, and availability, as well as adhering to appropriate data protection requirements. In a nutshell, cloud data protection seeks to protect data against unauthorized access, deletion, or breaches while retaining its accuracy and accessible to authorized users. This is accomplished in the cloud environment using various security measures, encryption approaches, access controls, disaster recovery and backup processes, and constant monitoring and threat detection. The research significance of data protection in the cloud environment can be summarized as follows: Security and Privacy: Research in data protection in the cloud helps address the security and privacy concerns associated with storing and processing sensitive data in cloud-based systems. It explores and develops advanced security mechanisms, encryption techniques, and access controls to protect data from unauthorized access, data breaches, and privacy violations. Trust and Confidence: Research in data protection contributes to building trust and confidence in cloud computing. By developing robust security solutions and demonstrating their effectiveness, research helps alleviate concerns about data security and privacy, fostering greater adoption of cloud services by organizations and individuals. Compliance and Regulations: Cloud computing often involves compliance with data protection regulations and industry standards. Research in this area explores the legal and regulatory aspects of data protection in the cloud and helps organizations understand and comply with relevant requirements. Data Resilience and Recovery: Research in data protection focuses on ensuring data resilience and developing efficient data recovery mechanisms in the cloud. It explores backup and disaster recovery strategies, data replication techniques, and data loss prevention methods to minimize downtime, recover data promptly, and maintain business continuity in the event of system failures or disasters. By addressing these research areas, studies on data protection in the cloud environment contribute to enhancing security, privacy, compliance, and resilience in cloud computing. They provide valuable insights, practical solutions, and guidelines for organizations and service providers to protect data effectively and maintain the trust of users in cloud-based services. The weighted product method approach is commonly used to choose the best data protection in cloud environment. CCSS1, CCSS2, CCSS3, CCSS4, CCSS5 data visibility, data integrity, Maintains compliance, Data security, Data storage. From the result it is seen that CCSS2 got highest rank whereas CCSS5 got lowest rank According to the results, CCSS2 was ranked first.

A Hierarchical Blockchain-Based Trust Measurement Method for Drone Cluster Nodes

In response to the challenge of low accuracy in node trust evaluation due to the high dynamics of entry and exit of drone cluster nodes, we propose a hierarchical blockchain-based trust measurement method for drone cluster nodes. This method overcomes the difficulties related to trust inheritance for dynamic nodes, trust re-evaluation of dynamic clusters, and integrated trust calculation for drone nodes. By utilizing a multi-layer unmanned cluster blockchain for trusted historical data storage and verification, we achieve scalability in measuring intermittent trust across time intervals, ultimately improving the accuracy of trust measurement for drone cluster nodes. We design a resource-constrained multi-layer unmanned cluster blockchain architecture, optimize the computing power balance within the cluster, and establish a collaborative blockchain mechanism. Additionally, we construct a dynamic evaluation method for trust in drone nodes based on task perception, integrating and calculating the comprehensive trust of drone nodes. This approach addresses trusted sharing and circulation of task data and resolves the non-inheritability of historical data. Experimental simulations conducted using NS3 and MATLAB demonstrate the superior performance of our trust value measurement method for unmanned aerial vehicle cluster nodes in terms of accurate malicious node detection, resilience to trust value fluctuations, and low resource delay retention.

A blockchain-empowered framework for decentralized trust management in Internet of Battlefield Things

The security of the Internet of Battlefield Things (IoBT) is greatly challenged by malicious intrusions from external environments. To protect IoBT from such intrusions, it is crucial to implement trust management (TM) at the node level. However, the decentralized and dynamic nature of IoBT means that participating nodes have no predetermined trust relationships. To address this issue, we propose a blockchain-empowered framework to achieve decentralized TM in IoBT. We begin by introducing a network model that integrates the space and ground segments with portable ping-pong stations. In the absence of fixed infrastructure, these stations enable rapid networking among combat units in IoBT. We also leverage these stations to form a blockchain network that manages decentralized trusts among IoBT nodes. Our blockchain-empowered TM framework consists of three algorithmic components: (i) Local Trust Computation: each IoBT node uses a local trust computation algorithm to compute the local trusts of its neighboring nodes; (ii) Global Trust Aggregation: portable ping-pong stations use a global trust aggregation algorithm to aggregate the global trusts of IoBT nodes; (iii) Blockchain Consensus Process: the global trusts of IoBT nodes are updated to the blockchain. We have also developed a new consensus algorithm that incentivizes blockchain nodes to proactively maintain the real trusts of IoBT nodes. We conducted several simulations incorporating different attack models of malicious nodes and system setups to comprehensively evaluate the performance of our TM framework. The simulation results demonstrate that our blockchain-empowered decentralized TM effectively suppresses the probability of successful attacks and thus improves the security of IoBT. In summary, our proposed framework offers a promising solution for achieving decentralized TM in IoBT, and we believe that it has the potential to significantly enhance the security of this rapidly developing field.

Recommendation-based trust computation and rating prediction model for security enhancement in cloud computing systems

Recommendation-based trust computation and rating prediction model for security enhancement in cloud computing systems

Multi level trust calculation with improved ant colony optimization for improving quality of service in wireless sensor network

Wireless sensor network (WSN) is the most integral parts of current technology which are used for the real time applications. The major drawbacks in currect technologies are threads due to the creation of false trust values and data congestion. Maximum of the concept of WSNs primarily needs security and optimization. So, we are in the desire to develop a new model which is highly secured and localized. In this paper, we introduced a novel approach namely multi level trust calculation with improved ant colony optimization (MLT-IACO). This approach mainly sub-divided into two sections they are multi level trust calculation which is the combination three levels of trust such as direct trust, indirect trust and random repeat trust. Secondly, improved ant colony optimization technique is used to find the optimal path in the network. By transmitting the data in the optimal path, the congestion and delay of the network is reduced which leads to increase the efficiency. The outcome values are comparatively analyzed based the parameters such as packet delivery ratio, network throughput and average latency. While compared with the earlier research our MLT-IACO approach produce high packet delivery ratio and throughput as well as lower latency and routing overhead.

A Novel Multiple Role Evaluation Fusion-Based Trust Management Framework in Blockchain-Enabled 6G Network.

Six-generation (6G) networks will contain a higher density of users, base stations, and communication equipment, which poses a significant challenge to secure communications and collaborations due to the complex network and environment as well as the number of resource-constraint devices used. Trust evaluation is the basis for secure communications and collaborations, providing an access criterion for interconnecting different nodes. Without a trust evaluation mechanism, the risk of cyberattacks on 6G networks will be greatly increased, which will eventually lead to the failure of network collaboration. For the sake of performing a comprehensive evaluation of nodes, this paper proposes a novel multiple role fusion trust evaluation framework that integrates multiple role fusion trust calculation and blockchain-based trust management. In order to take advantage of fused trust values for trust prediction, a neural network fitting method is utilized in the paper. This work further optimizes the traditional trust management framework and utilizes the optimized model for node trust prediction to better increase the security of communication systems. The results show that multiple role fusion has better stability than a single role evaluation network and better performance in anomaly detection and evaluation accuracy.

An intelligent active probing and trace-back scheme for IoT anomaly detection

Due to their simple hardware, sensor nodes in IoT are vulnerable to attack, leading to data routing blockages or malicious tampering, which significantly disrupts secure data collection. An Intelligent Active Probing and Trace-back Scheme for IoT Anomaly Detection (APTAD) is proposed to collect integrated IoT data by recruiting Mobile Edge Users (MEUs). (a) An intelligent unsupervised learning approach is used to identify anomalous data from the collected data by MEUs and help to identify anomalous nodes. (b) Recruit MEUs to trace back and propose a series of trust calculation methods to determine the trust of nodes. (c) The last, the number of active detection packets and detection paths are designed, so as to accurately identify the trust of nodes in IoT at the minimum cost of the network. A large number of experimental results show that the recruiting cost and average anomaly detection time are reduced by 6.5 times and 34.33% respectively, while the accuracy of trust identification is improved by 20%.

Context-Based Adaptive Fog Computing Trust Solution for Time-Critical Smart Healthcare Systems

Fog’s inherent decentralized nature and ability to process data in transit, i.e., the ability to draw conclusions in real-time, are quite suitable for scenarios where an enormous number of decentralized devices need to communicate and provide live analysis of data and storage tasks. Fog computing’s ability to work close to the end user and non-reliance on centralized architecture provide the dependability that time-critical smart healthcare systems need. Because of the critical nature of healthcare data, better security and privacy solutions for fog computing are required, with trust being of the utmost importance. Context-dependent trust solution for fogs is still an open research area, so the aim of this research is to propose a context-based adaptive trust solution for smart healthcare environments using a Bayesian approach and similarity measures. The proposed trust model has been simulated in Contiki, Cooja, and a Java-based application has been developed to analyze our results. Adaptive weights assigned to direct and indirect trust using entropy values ensure minimization of trust bias as opposed to static weighting. Context-based similarity calculations filter out recommender nodes with malicious intent using server, social contact, and service similarity. This model is efficient and has a low trust computation overhead because it has a liner complexity of O(n).

A multi-dimensional trust model for misbehavior detection in vehicular ad hoc networks

Communication security is one of the focus issues of vehicular ad hoc networks (VANETs). Trust model is commonly used to identify malicious attackers or filter false messages in the network, which effectively guarantees secure communication between vehicles. Constrained by the distributed network architecture of VANETs, trust evaluation results may be biased due to uneven data distribution and lack of reference. By colluding with the partners, malicious vehicle launches imbalance attack and zigzag attack to bypass the detection of the trust model. To address these new intelligent attacks, we propose a multi-dimensional trust model (MDT) in this paper, which makes a combined evaluation of different trust attributes from vehicles. In MDT, data collection and trust calculation are deployed in vehicles and trust authority (TA), respectively. Since the model dynamically adjusts the corresponding weight coefficient according to the data fluctuations of each trust indicator by adopting entropy weight method and filters anomalous evaluation results by using the median absolute deviation (MAD) algorithm, it can effectively deal with complex and variable intelligent attacks. The legitimate identity of a vehicle will be revoked from the network by TA once its trust value is less than the threshold. We have conducted extensive experimental studies to evaluate the performance of MDT. The experimental results show that the MDT model can accurately and effectively calculate the global trust of vehicles and detect malicious attacks in the network under different scenarios and tests.

DLFTI: A deep learning based fast truth inference mechanism for distributed spatiotemporal data in mobile crowd sensing

The paradigm of Mobile Crowd Sensing (MCS) allows for numerous applications with distributed spatiotemporal data, where great attention is drawn to the fundamental problems for truth inference. Existing works all suffer from poor accuracy and slow start, due to the lack of valid information for Ground Truth Data (GTD) and workers. These problems make the MCS system vulnerable to fraudulent attacks by malicious gangs, causing the Estimated Truth Data (ETD) to deviate significantly from GTD. In this paper, we propose a Deep Learning based Fast Truth Inference mechanism, called DLFTI, to achieve fast trust computing and accurate truth discovery in MCS. First, we introduce the Degrees-Of-Trust (DOT) to characterize the sensing ability of workers and establish worker profiles based on DOT to recognize workers’ trustworthiness dynamically. Then, we abandon the unrealistic assumption of priori GTD in previous studies and instead utilize the Unmanned Aerial Vehicles (UAVs), recognized trustworthy workers and the Deep Matrix Factorization (DMF) method to construct three-level GTD and three-level ETD, which are used for fast trust computing of workers and accurate truth discovery of tasks respectively. Finally, we conduct extensive simulations on a real-world dataset to corroborate the significant performance of DLFTI.

Cloud Services User’s Recommendation System Using Random Iterative Fuzzy-Based Trust Computation and Support Vector Regression

Cloud computing is now a fundamental type of computing due to technological innovation and it is believed to be a benefit for mid-scale enterprises. The use of cloud computing is increasing daily, which improves service quality but also gives rise to security concerns. Finding trustworthy service can be very challenging, take a great deal of time, or produce subpar services. Due to these difficulties, the client needs a service that is dependable, suitable, time-saving, and trustworthy. As a result, from the end user’s perspective, adopting a cloud service’s trustworthiness becomes crucial. Trust is a measure of how well users’ expectations about a service’s capabilities are realized. In this research, a recommendation system for cloud service customers based on random iterative fuzzy computation (RIFTC) is proposed. RIFTC focuses on the assessment of trust using Quality of Service (QoS) characteristics. RIFTC calculates trust using the machine learning approach Support Vector Regression (SVR). RIFTC can helpfully recommend a cloud service to the end user and anticipate the trust values of cloud services.. Precision (97%), latency (51%), throughput (25.99 mbps), mean absolute error (54%), and re-call (97%) rates are used to assess how well this recommendation system performs. RIFTC’s average F-measure rate is calculated by adjusting the number of users from 200 to 300, and it is 93.46% more accurate on average with less time spent than the current methodologies.

VANET Secure Reputation Evaluation & Management Model Based on Double Layer Blockchain

Vehicle ad-hoc network (VANET) is interconnected through message forwarding and exchanging among vehicle nodes. Due to its highly dynamic topology and its wireless and heterogeneous communication mode, VANET is more vulnerable to security threats from multiple parties. Compared to entity-based security authentication, it is essential to consider how to protect the security of the data itself. Existing studies have evaluated the reliability of interactive data through reputation quantification, but there are still some issues in the design of secure reputation management schemes, such as its low efficiency, poor security, and unreliable management. Aiming at the above-mentioned issues, in this paper we propose an effective VANET model with a secure reputation based on a blockchain, and it is called the double-layer blockchain-based reputation evaluation & management model (DBREMM). In the DBREMM, we design a reputation management model based on two parallel blockchains that work collaboratively, and these are called the event chain and reputation chain. A complete set of reputation evaluation schemes is presented. Our schemes can reduce observation errors and improve evaluation reliability during trust computation by using direct trust calculation based on the multi-factor Bayesian inference. Additionally, we propose an indirect trust calculation based on the historical accumulated reputation value with an attenuation factor, and a secure a reputation fusion scheme based on the number threshold with the fluctuation factor, which can reduce the possibility of attacks, such as collusive attacks and false information injection. Theoretical analysis and extensive simulation experiments reflect the DBREMM’s security algorithm effectiveness, accuracy, and ability to resist several attacks.

A TRUSTED COMPUTING RESOURCES OPTIMAL SCHEDULING ALGORITHM IN INDUSTRIAL INTERNET AND HEALTHCARE INTEGRATING DRL, BLOCKCHAIN AND END-EDGE-CLOUD

With the rapid development of the Internet of Things (IoT) and Internet technology, the product of the combination of the two, the Industrial Internet, has also received extensive attention and there are more and more research achievements related to the Industrial Internet. In the industrial Internet system, the communication network system composed of sensors, communication nodes, controllers and other intelligent devices can realize efficient and convenient data interaction between people and machines, providing an important infrastructure and technical support for industrial big data analysis and intelligent production. However, in the current industrial Internet system, industrial equipment users generally have the problem of low computing energy efficiency, and the collected industrial data has a high-security risk in the transmission, processing and other processes. At the same time, the size and scale of the industrial Internet equipment group is huge, and the lack of rational resource allocation leads to excessive waste of computing resources in the system, which is also a prominent problem of the current industrial Internet system. In response to the above questions, this paper, on the basis of reading a large number of documents, integrates the improved DRL algorithm, End-Edge-Cloud architecture and blockchain to form a new industrial Internet architecture. The architecture realizes computing offload through the three-tier structure of terminal layer, edge layer and cloud layer, and guarantees the security of industrial data through the decentralized feature of blockchain, ultimately achieving the goal of reducing energy consumption, computing overhead and trusted computing. In the architecture proposed in this paper, the dynamic unloading of industrial data and computing tasks is achieved through a three-tier architecture. The MDP is used to build an optimization problem model, and the improved DRL algorithm is used to iteratively solve the optimal computing resource scheduling strategy. The main research contents of this paper include (1) Using MDP to model optimization problems; (2) Propose an industrial Internet system architecture that integrates and improves DRL, “end edge cloud” and blockchain; (3) The MDP problem is solved iteratively based on deep reinforcement learning. The simulation results show that the proposed architecture has more advantages than the existing six architectures in terms of computing cost, equipment energy consumption and total working time.

Trust Computational Model for IoT using Machine Learning

Aims and Background: The Internet of Things has evolved over the years to a greater extent, where objects communicate with each other over a network. Heterogenous communication between the nodes leads to a large amount of information sharing, and sensitive information could be shared over the network. It is important to maintain privacy and security during information sharing to protect devices from communicating with malicious nodes. Objectives and Methodology: The concept of trust was introduced to prevent nodes from communicating with malicious nodes. A trust computation model for the IoT based on machine learning concepts was designed, which evaluates trust based on the Trust Marks. There are three trust marks, out of which two are evaluated. The three trust marks are knowledge, experience, and reputation. Knowledge trust marks are evaluated separately based on their trust property mathematical formulations, and then based on these properties, machine learning-based algorithms are applied to train the model to classify the objects as trustworthy and untrustworthy. Results: The effectiveness of the Knowledge Trust Mark is measured by a simulation and confusion matrix. The accuracy of the trained model is shown by the accuracy score. The trust computational model for IoT using machine learning shows higher accuracy in classifying the objects as trustworthy and untrustworthy. Conclusion: The experience trust mark is evaluated based on its properties, and the behaviour of the experience is shown over time graphically.

BFT-IoMT: A Blockchain-Based Trust Mechanism to Mitigate Sybil Attack Using Fuzzy Logic in the Internet of Medical Things.

Numerous sensitive applications, such as healthcare and medical services, need reliable transmission as a prerequisite for the success of the new age of communications technology. Unfortunately, these systems are highly vulnerable to attacks like Sybil, where many false nodes are created and spread with deceitful intentions. Therefore, these false nodes must be instantly identified and isolated from the network due to security concerns and the sensitivity of data utilized in healthcare applications. Especially for life-threatening diseases like COVID-19, it is crucial to have devices connected to the Internet of Medical Things (IoMT) that can be believed to respond with high reliability and accuracy. Thus, trust-based security offers a safe environment for IoMT applications. This study proposes a blockchain-based fuzzy trust management framework (BFT-IoMT) to detect and isolate Sybil nodes in IoMT networks. The results demonstrate that the proposed BFT-IoMT framework is 25.43% and 12.64%, 12.54% and 6.65%, 37.85% and 19.08%, 17.40% and 8.72%, and 13.04% and 5.05% more efficient and effective in terms of energy consumption, attack detection, trust computation reliability, packet delivery ratio, and throughput, respectively, as compared to the other state-of-the-art frameworks available in the literature.