Trust Management Framework Research Articles

Multi Attribute Trust Aware Routing for Adversary Detection in Wireless Sensor Networks

Security attacks has become major obstacles in Wireless Sensor Networks (WSN) and Trust Aware Routing is second line of defense. With an aim to improve on the existing routing mechanisms, in this paper, we propose Interactive, Onlooker and Capability Trust Aware Routing (IOC-TAR), a multi-trust attribute framework for trust management in WSNs. IOC-TAR employs three trust features to establish a trustworthy relationship between sensor nodes for their cooperation. Interactive trust uses communication interactions, onlooker trust uses neighbor node’s opinions and capability trust uses stability and fault tolerance for trust assessment. For, each node, one composite trust factor is formulated and decides its trustworthiness. Extensive simulation experiments are conducted to evaluate the effectiveness and efficiency of proposed IOC-TAR in the identification of malicious nodes and the provision of attack resilience. The results declare that the IOC-TAR enhances the attack resilience by improving Malicious Detection rate and reducing False Positive Rate.

SLA-Driven Trust and Reputation Management Framework for 5G Distributed Service Marketplaces

SLA-Driven Trust and Reputation Management Framework for 5G Distributed Service Marketplaces

A Blockchain-Enabled Trust Management Framework for Energy-Efficient and Secure Routing in Mobile Ad-Hoc Networks

This study principally addresses the challenges associated with streaming videos in wireless Mobile ad hoc networks (MANETs), primarily constrained by wireless channels and node mobility. This study proposes a novel routing protocol incorporating a feedback mechanism and cross-layer architecture, supported by three key enhancements. First, it incorporates a route recovery strategy for detecting link failures and re-establishing connections based on predefined Quality of Service (QoS) metrics. Second, a novel algorithm estimates the available bandwidth, enabling dynamic adaptation of bitrate at the application layer concerning the source video. This novel rate-adaptive approach utilizes a scalable layered structure of the video coding, thereby removing layers that the network cannot efficiently support due to bandwidth limitations. Third, a gateway discovery procedure enhances the connectivity among MANETs and other infrastructure networks. This method employs available bandwidth as a parameter while selecting appropriate gateways and dynamically adjusts operational parameters like proactive area size and advertisement message frequency. The performances of the proposed Enhanced OLSR (E-OLSR) protocol are compared with the state-of-the-art routing procedures such as Adhoc on-demand distance vector (AODV), destination sequenced distance vector (DSDV), and optimized link state routing (OLSR), in terms of metrics like packet delivery ratio (PDR), end-to-end (E2E) delay, throughput, as well as energy consumption. The simulation results demonstrated notable improvements, including reduced packet delay, fewer dropped packets, and decreased link failures, indicating effective utilization of available bandwidth. The overall performance of the proposed E-OLSR protocol exceeds other traditional protocols and its security is enhanced through the integration of Blockchain Technology.

Deep Trust: A Novel Framework for Dynamic Trust and Reputation Management in the Internet of Things (IoT) Based Networks

Deep Trust: A Novel Framework for Dynamic Trust and Reputation Management in the Internet of Things (IoT) Based Networks

GALTrust: Generative Adverserial Learning-Based Framework for Trust Management in Spatial Crowdsourcing Drone Services

GALTrust: Generative Adverserial Learning-Based Framework for Trust Management in Spatial Crowdsourcing Drone Services

Trust Management in Cloud Computing Using Blockchain Technology: a taxonomy, review & future directions

Through visualization and resource integration, cloud computing has expanded its service its service area and offers a better user experience than the traditional platform, along with us business operation model bringing huge economic and social benefits. However, a large amount of evidence shows that cloud computing is facing with serious security and trust crises, and building a trust / enabled transaction environment has become its key factor. The traditional cloud trust model transparency and traceability trust evolution results cannot be fully recognized by all participants. Its unique features in operating rules and traceability of records ensure the integrity, undesirability and security of the transaction data. Therefore Blockchain is very suitable for constructing distributed and decentralized trust architecture. Based on a novel Cloud- Edge trust management framework and a double Blochkchain structure based cloud transaction model , it identifies the open challenges and gives direction for future research field.

Enhancing Privacy Preservation in Vehicular Trust Management Systems through Blockchain Technology

The advent of the Internet of Vehicles (IoV) has led to a significant surge in data generation from vehicles, prompting the innovative utilization of data transactions within the IoV environment. However, due to the inherent trustless nature of data transactions in IoV, concerns have arisen regarding the lack of trust between involved parties and the potential compromise of user privacy. To address these issues, blockchain technology emerges as a suitable decentralized distributed storage and security management solution, offering transparency and security in data transactions. In this study, we leverage blockchain to integrate with the IoV, devising a robust trust management framework, and devising a privacy protection scheme to safeguard user privacy concerns. Additionally, we employ the performance evaluation process algebra (PEPA) method for system modeling and performance analysis to assess the efficacy of our proposed solution. Empirical findings demonstrate that our approach effectively enhances the performance of data transactions within the IoV while ensuring that the privacy of users remains intact.

A trust-aware openflow switching framework for software defined networks (SDN)

Software Defined Networks (SDN) and Network Function Virtualisation (NFV) are prime driving technologies behind 5G and Beyond 5G (B5G) communications. The network control intelligence segregation in the SDN infrastructure enables dynamic network features (such as dynamic end-to-end management of security and quality of service (QoS)) offering significantly improved network performance. Even if one assumes that the centralised SDN controller can be security hardened and hence can be trusted, a fundamental challenge in such networks is that the data plane and switching devices are susceptible to cyberattacks. A malicious adversary can compromise them during run-time making them unreliable for secure and trusted communications. Furthermore, the controller communicating with OpenFlow switching devices is unable to accurately assess the state of the switching devices, which serves as the communication base for NFVs in 5G networks. Vulnerable switching devices can put the whole 5G network infrastructure at risk. Hence, there is a clear need for the controller and the management layer to determine the trustworthiness of the switching devices at run-time. The current trend is for many such devices to deploy trusted computing functionality such as Trusted Platform Module (TPM) or Software Guard Extension (SGx) to achieve local as well as remote attestation. In this paper, we present a dynamic trust management framework for evaluating the trustworthiness of the OpenFlow switching devices deployed in the SDN based networks. We formulate device properties that need to be assessed to determine the trust status of the device. We develop a trust enhanced security architecture which can be used to evaluate the trustworthiness of devices and determine their deployment in the provision of network services. The proposed framework uses subjective logic based techniques to derive trust levels of the switching devices at run-time, which are then used by the architecture to make trust enhanced decisions on the provision of network services. A prototype implementation of the proposed architecture is described, which demonstrates how the trustworthiness of the OpenFlow devices are assessed at run-time. The paper concludes with the performance and security analysis of the implemented trust enhanced architecture services.

MSLShard: An efficient sharding-based trust management framework for blockchain-empowered IoT access control

The Internet of Things (IoT) is widely used in modern smart areas such as smart cities, but data security remains a significant challenge. Blockchain-based IoT access control addresses data security issues by preventing unauthorized devices from accessing limited IoT resources. However, most existing blockchain-based access control schemes still have scalability and security issues. A lightweight and secure framework, MSLShard, is proposed for the blockchain-based access control via sharding, which consists of two parts. First, an adaptive network sharding scheme based on network distance, node credibility, and access frequency is designed in the cloud-edge-end collaboration-based IoT architecture to reduce nodes' storage pressure and improve the scheme's scalability. Second, the trust management based on multidimensional subjective logic explicitly portrays the heterogeneity among edge nodes to motivate cooperation and further improve the security of the blockchain. Experimental results show that MSLShard can achieve high scalability and security in large-scale IoT environments compared to existing methods.

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.

An End-to-end Trust Management Framework for Crowdsourced IoT Services

We propose a novel end-to-end trust management framework for crowdsourced Internet of Things (IoT) services. The framework targets three main aspects:trust assessment,trust information credibility and accuracy, andtrust information storage. We harness theusage patternsof IoT consumers to offer a trust assessment thatadaptsto IoT consumers’ uses. Additionally, our framework ascertains thecredibilityandaccuracyof trust-related information before trust assessment. This is achieved by validating the data collected by IoT consumers and providers. In addition, our framework ensures thecontextual fairnessbetween IoT services and trust information. Moreover, we propose a blockchain-based trust information storage approach. Our proposed storage solution preserves theintegrityandavailabilityof trust information.

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.

A Realtime Adaptive Trust Model Based on Artificial Neural Networks for Wireless Sensor Networks

Wireless sensor networks (WSNs) are vulnerable to security attacks due to the unbounded nature of the wireless medium, restricted node resources, and cooperative routing. Standard cryptography and authentication mechanisms help protect against external attacks, but a compromised node can easily bypass them. This work aims to protect WSNs against internal attacks, which are mostly launched from compromised nodes to disrupt the network’s operation and/or reduce its performance. The trust and reputation management framework provides a routing cost function for selecting the best secure next hop. Tuning the trust weights is essential to cope with the constant changes in the network environment, such as the sensor nodes’ behaviours and locations. To allow real-time operation, the proposed framework introduces an artificial neural network (ANN) in each sensor node that automatically adjusts the weights of the considered trust metrics according to the WSN state. A large dataset is generated to train and test the ANN using a multitude of simulated cases. A prototype is developed and tested using the J-Sim simulator to show the performance gain resulting from applying the adaptive trust model. The experimental results showed that the adaptive model has robust performance and has achieved an improved packet delivery ratio with reduced power consumption and reduced average packet loss. The results showed that when sensor nodes were static and malicious nodes were present, the average accuracy was 99.6%, while when they were in motion, it was 88.1%.

Current security and privacy posture in wireless body area networks

Wireless Body Area Networks (WBANs) have emerged as a promising technology for remote health monitoring and healthcare applications. However, ensuring the security and privacy of sensitive health data in WBANs is crucial to foster user trust and prevent unauthorized access or data breaches. This paper provides an overview of the key challenges, techniques, and research gaps in WBAN security and privacy. The findings indicate that the challenges in WBAN security and privacy include resource constraints, compatibility issues, privacy concerns, dynamic network environments, security and usability trade-offs, emerging threat landscape, and user awareness and education. To address these challenges, various security techniques have been developed, such as authentication and authorization mechanisms, encryption, access control, secure communication protocols, intrusion detection systems, and privacy-preserving data handling techniques. Despite the progress made, there are research gaps that require further investigation. These research gaps include the development of secure and lightweight authentication mechanisms, privacy-preserving data analysis techniques, trust and security management frameworks, resilience to insider threats, security of data aggregation and fusion, user-centric security designs, and addressing legal and ethical considerations. Addressing these research gaps and challenges requires collaboration between researchers, device manufacturers, policymakers, and end-users. Ongoing research and innovation are necessary to develop robust security techniques, privacy-enhancing technologies, and user-friendly solutions tailored for WBANs. Additionally, compliance with privacy regulations, user education, and awareness are critical for responsible and ethical use of WBANs.

HIDE-Healthcare IoT Data Trust ManagEment: Attribute centric intelligent privacy approach

The cloud-based Internet of Things (IoTs) storage enables patients to monitor their health remotely and offers services for physicians of various Medical Institutions (MIs) to diagnose and treat them on time. As a matter of trust, patients are legally expected to hide their real identity and ensure data privacy in the cross-domain of IoT-healthcare, whether it is stored correctly or modified due to external and internal attacks in the cloud. Additionally, physicians treat patients and continuously store duplicated data in cloud storage, which increases the cost of computing. In this context, this paper presents HIDE-Healthcare IoT Data privacy trust management framework, focusing on attributes. Patients’ attributes are used to encrypt and decrypt sensory data between patients and different entities by incorporating the idea of trustworthy and secure shared keys. HIDE uses an intelligent object’s pointer to store the same patient’s sensory data in various versions to prevent data duplication, which will help track MIs that treat patients. An intelligent content-based emergency data access control is developed to monitor multiple patient health criticalities in HIDE. The security analysis and experimental evaluation attest to the benefits of the proposed HIDE framework, considering security and privacy metrics.

Blockchain-Assisted Privacy-Preserving and Context-Aware Trust Management Framework for Secure Communications in VANETs.

Vehicular ad hoc networks (VANETs) are used for improving traffic efficiency and road safety. However, VANETs are vulnerable to various attacks from malicious vehicles. Malicious vehicles can disrupt the normal operation of VANET applications by broadcasting bogus event messages that may cause accidents, threatening people's lives. Therefore, the receiver node needs to evaluate the authenticity and trustworthiness of the sender vehicles and their messages before acting. Although several solutions for trust management in VANETs have been proposed to address these issues of malicious vehicles, existing trust management schemes have two main issues. Firstly, these schemes have no authentication components and assume the nodes are authenticated before communicating. Consequently, these schemes do not meet VANET security and privacy requirements. Secondly, existing trust management schemes are not designed to operate in various contexts of VANETs that occur frequently due to sudden variations in the network dynamics, making existing solutions impractical for VANETs. In this paper, we present a novel blockchain-assisted privacy-preserving and context-aware trust management framework that combines a blockchain-assisted privacy-preserving authentication scheme and a context-aware trust management scheme for securing communications in VANETs. The authentication scheme is proposed to enable anonymous and mutual authentication of vehicular nodes and their messages and meet VANET efficiency, security, and privacy requirements. The context-aware trust management scheme is proposed to evaluate the trustworthiness of the sender vehicles and their messages, and successfully detect malicious vehicles and their false/bogus messages and eliminate them from the network, thereby ensuring safe, secure, and efficient communications in VANETs. In contrast to existing trust schemes, the proposed framework can operate and adapt to various contexts/scenarios in VANETs while meeting all VANET security and privacy requirements. According to efficiency analysis and simulation results, the proposed framework outperforms the baseline schemes and demonstrates to be secure, effective, and robust for enhancing vehicular communication security.

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.

Reputation based novel trust management framework with enhanced availability for cloud

To select a cloud service provider (CSP) out of immense number of relevant CSPs available, cloud customer (CC) trusts on the reviews provided by the CCs who have used the services provided by CSPs. However, relying on these feedbacks may be hazardous if the review(s) have been deliberately posted with wicked motive. To mitigate the impact of malicious feedbacks, a framework, namely highly available credible cloud trust management framework (HA2CTMF) has been proposed with the intent of revamping the reputation and availability parameters of QoS by evaluating the trust of cloud service providers (CSPs) based on the credibility of feedbacks furnished by CC. HA2CTMF detects collusion and Sybil attacks on the labeled CloudArmor dataset and increases availability of the trust service. Collusion attack has been detected by comparing review count; service count; & mean of reviews with their respective threshold values. Further, Sybil attack has been uncovered with the help of these aspects: deviation from mean of reviews; pattern detection; & comparison of polarity value from both lower & upper threshold values. Additionally, enhanced availability has been ensured by working on disaster management and diminishing repercussions of single point of failure. The results have been accessed on the following parameters: precision; recall; f1-score & availability percentage on the dataset comprising 8774 data points. This framework has exhibited precision of 85.69%, recall of 89.45%, f1-score of 87.53% and availability of 96.71% compared to CloudArmor framework values on the same parameters as: 44.47%; 62.21%; 51.85% & 70.43% respectively. As per the results attained, we conclude that proposed HA2CTMF has demonstrated better outcome.

Fedidchain: An Innovative Blockchain-Enabled Framework for Cross-Border Interoperability and Trust Management in Identity Federation Systems

Fedidchain: An Innovative Blockchain-Enabled Framework for Cross-Border Interoperability and Trust Management in Identity Federation Systems

A Trust Management Framework for Vehicular Ad Hoc Networks

Vehicular Ad Hoc Networks (VANETs) enable road users and public infrastructure to share information that improves the operation of roads and driver experience. However, these are vulnerable to poorly behaved authorized users. Trust management is used to address attacks from authorized users in accordance with their trust score. By removing the dissemination of trust metrics in the validation process, communication overhead and response time are lowered. In this paper, we propose a new Tamper-Proof Device (TPD) based trust management framework for controlling trust at the sender side vehicle that regulates driver behaviour. Moreover, the dissemination of feedback is only required when there is conflicting information in the VANET. If a conflict arises, the Road-Side Unit (RSU) decides, using the weighted voting system, whether the originator is to be believed, or not. The framework is evaluated against a centralized reputation approach and the results demonstrate that it outperforms the latter.