Trust Management Mechanism Research Articles

Lightweight, Trust-Managing, and Privacy-Preserving Collaborative Intrusion Detection for Internet of Things

This research introduces a comprehensive collaborative intrusion detection system (CIDS) framework aimed at bolstering the security of Internet of Things (IoT) environments by synergistically integrating lightweight architecture, trust management, and privacy-preserving mechanisms. The proposed hierarchical architecture spans edge, fog, and cloud layers, ensuring efficient and scalable collaborative intrusion detection. Trustworthiness is established through the incorporation of distributed ledger technology (DLT), leveraging blockchain frameworks to enhance the reliability and transparency of communication among IoT devices. Furthermore, the research adopts federated learning (FL) techniques to address privacy concerns, allowing devices to collaboratively learn from decentralized data sources while preserving individual data privacy. Validation of the proposed approach is conducted using the CICIoT2023 dataset, demonstrating its effectiveness in enhancing the security posture of IoT ecosystems. This research contributes to the advancement of secure and resilient IoT infrastructures, addressing the imperative need for lightweight, trust-managing, and privacy-preserving solutions in the face of evolving cybersecurity challenges. According to our experiments, the proposed model achieved an average accuracy of 97.65%, precision of 97.65%, recall of 100%, and F1-score of 98.81% when detecting various attacks on IoT systems with heterogeneous devices and networks. The system is a lightweight system when compared with traditional intrusion detection that uses centralized learning in terms of network latency and memory consumption. The proposed system shows trust and can keep private data in an IoT environment.

A fuzzy trust management mechanism with dynamic behavior monitoring for wireless sensor networks

A fuzzy trust management mechanism with dynamic behavior monitoring for wireless sensor networks

Cooperative-centrality enabled investigations on edge-based trustworthy framework for cloud focused internet of things

Trustworthiness possesses numerous design challenges for the researchers and scientists in the global era of internet of things. The solution to deal with these challenges lies in trust management mechanisms (TMM). The integration of TMM on the Internet of things (IoT) adds a higher level of security and reliability for the resource utilization and management. On the other hand, the efficient and optimal implementation of TMM in IoT network requires significant efforts and innovations. In this paper, an innovative edge based trustworthy framework (EBTF) using novel cooperative centrality-based system (NCCBS) has been proposed and implemented for IoT networks. Trustworthiness has been computed using factors like direct trust, recommendation, global trust, and centralities. The NCCBS has been compared with existing mechanism namely: Distributive Reputation Management (DRM), Personalized Similarity Measure (PSM), and Reliable Trust Computation Mechanism (RTCM). The NCCBS outperformed the existing approaches with the adoption of dynamic weight computation scheme. The performance of IoT networks has been evaluated on parameters like recommendation weight, trust index, interaction success rate and malicious behaviour analysis. Simulations have been carried out to evaluate and validate the proposed NCCBS and it depicts optimal performance with reference to the existing schemes.

Trust management systems in Wireless Sensor Networks

The article begins by offering a comprehensive review of the current research landscape concerning trust management mechanisms. It elucidates the foundational concepts behind trust management mechanisms, subsequently detailing various attack models and the inherent vulnerabilities they exploit. A significant portion of the discussion delves into the primary computational methodologies employed in trust management. These encompass a range of techniques such as Bayesian statistics, subjective logic, fuzzy logic, D-s evidence theory, entropy theory, cloud theory, hierarchical analysis, fog computing, and machine learning. From this foundational understanding, the piece transitions to outline the challenges poised to shape the evolution of trust management mechanisms. This section not only emphasizes the hurdles currently faced by researchers and practitioners but also attempts to forecast the potential obstacles of the future. In culmination, the article encapsulates both the current state of research and the anticipated directions that promise to steer the trajectory of trust management mechanisms in forthcoming years. This holistic perspective aims to provide readers with a clear roadmap of the field's progression, emphasizing both its achievements and the milestones yet to be attained.

ENHANCING SERVICE DISCOVERY IN MOBILE AD HOC NETWORKS USING SEMANTIC CLUSTERING AND HYBRID TRUST MANAGEMENT

Mobile Ad Hoc Networks (MANETs) facilitate communication among mobile devices without relying on fixed infrastructure. However, service discovery in MANETs faces challenges due to the dynamic topology and limited resources of nodes. Existing solutions often lack efficient resource utilization and fail to address trust management adequately. The conventional approaches for service discovery in MANETs are inefficient due to their inability to incorporate semantic clustering and robust trust management. Semantic clustering enhances the accuracy of service discovery by grouping nodes based on similar interests or functionalities. Meanwhile, traditional trust management mechanisms are inadequate in dynamic environments, leading to unreliable service discovery results. The proposed methodology involves developing a semantic clustering algorithm to organize nodes based on their semantic similarities. Additionally, a hybrid trust management system is implemented to assess the reliability of discovered services. The system combines both reputation-based and recommendation-based trust models to enhance the accuracy of trust evaluations. Through extensive simulations and experiments, the effectiveness of the proposed approach is demonstrated. The results indicate that the integration of semantic clustering and hybrid trust management significantly improves service discovery efficiency and reliability in MANETs. The approach achieves higher precision and recall rates compared to conventional methods, even under dynamic network conditions.

Reputation-based Raft-Poa layered consensus protocol converging UAV network

Integration of blockchain technology offers a robust solution for ensuring reliable data transmission within unmanned aerial vehicle (UAV) networks. Nevertheless, it is crucial to address challenges associated with UAV networks, including their dynamic nature, high latency, limited scalability, and algorithmic inefficiencies. Hence, our proposal involves integrating UAV networks with blockchain technology to enhance their performance by optimizing the consensus algorithm based on network efficiency. We first propose a comprehensive framework for optimizing network performance based on network tomography, incorporating a systematic analysis of network performance and fault avoidance strategies. To improve analysis of the core network’s real-time performance, we also propose a topology prediction method based on an effective probability matrix. Additionally, we establish a calculation method for optimally placing network monitoring points in the core network for enhanced efficiency. We then introduce an adjustable reputation-based Raft-Poa layered consensus protocol that improves consensus protocol scalability and efficiency. To tackle data and node security concerns, we further propose a trust management mechanism based on group signatures. This mechanism verifies the authenticity of node data and identifies malicious nodes. Finally, we validate the protocol’s effectiveness and compare the algorithm’s effectiveness with MMP, Raft, Poa, etc. The simulation results of this protocol show remarkable improvements in network communications and consensus process efficiency, and reductions in communication latency and node utilization rates.

Decentralized Trust Management and Incentive Mechanisms for Secure Information Sharing in VANET

Decentralized Trust Management and Incentive Mechanisms for Secure Information Sharing in VANET

The design of network security protection trust management system based on an improved hidden Markov model

With the growth of the Internet, network security issues have become increasingly complex, and the importance of node interaction security is also gradually becoming prominent. At present, research on network security protection mainly starts from the overall perspective, and some studies also start from the interaction between nodes. However, the trust management mechanisms in these studies do not have a predictive function. Therefore, to predict trust levels and protect network security, this paper innovatively proposes a trust management system for network security protection based on the improved hidden Markov model. The research divides the trust level of inter-node interactions by calculating the threat level of inter-node interactions and predicts the trust level of inter-node interactions through an optimized hidden Markov model. In addition, the study designs an estimation of the types of interactive threats between nodes based on alarm data. The research results show that when inactive interaction tuples are not excluded, the average prediction accuracy of the combined model is 95.5%. In response time, the maximum values of the active and passive cluster management pages are 38 ms and 33 ms, respectively, while the minimum values are 16 ms and 14 ms, with an average of 26.2 ms and 24 ms, respectively. The trust management system designed by the research institute has good performance and can provide systematic support for network security protection, which has good practical significance.

A survey of security, privacy and trust issues in vehicular computation offloading and their solutions using blockchain.

Continuous improvement in transportation systems and smart vehicles' appearance make new highly intensive applications. Complex applications need high-performance capabilities, real-time responses, and generate massive amounts of data to process and exchange. This presents the idea of vehicular edge computing (VEC), which is proposed to handle complex applications and satisfy smart vehicle processing requirements. VEC enables computation offloading to an edge server to reduce communication latency, execution cost and energy consumption greatly. However, offloading to another node opens up new vulnerabilities regarding security and privacy. Moreover, trust issues in such an untrustworthy environment need an effective trust management solution and incentive mechanisms to improve overall security. This will increase the computation offloading success rate and the vehicles' willingness to share their resources. Particularly given the high transportability and heterogeneity of vehicular networks, the conventional security and trust management methods are inadequate. Blockchain, the rapidly emerging trend technology, is a unique solution that can help overcome security and privacy issues and meet trust management and incentive mechanism goals. Blockchain's immutable distributed ledger, traceability, consensus validation system and smart contract features can improve vehicular network security. Although most research is focused on enhancing the performance of computation offloading algorithms, blockchain security solutions in computation offloading scenarios are not fully discussed. Thus, security and trust issues related to computation offloading in VEC environments need more consideration since supporting the new complex vehicular applications is essential. Therefore, this paper provides a review of recent surveys and studies, an overview of VEC, computation offloading and blockchain, in addition to discussing security, privacy and trust in vehicular networks and computation offloading while considering blockchain as a distributed security solution. We propose a new paradigm called blockchain edge of vehicle (BEoV) at the end, which enables several blockchain-based security services for vehicular computation offloading in particular.

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.

Main-Secondary Blockchain Framework: Cross-Domain Trust Management Mechanism Using Trust Ticket

Industrial Internet of Things (IIoT) can facilitate smart manufacturing and increase productivity through revolutionary techniques. As production processes are getting more sophisticated, an entire production process is usually completed collaboratively by multiple administrative domains (e.g., factories). Trust management is an effective solution to establish trust relationship between the devices from different administrative domains. Recently, many researchers integrate blockchain into trust management mechanisms. However, the existing methods have the shortcoming of low throughput, high delay and storage overhead, so cannot meet scalability requirements of large-scale IIoT multi-domains scenarios. Therefore, this paper proposes a master-slave chain framework to realize a scalable cross-domain trust management mechanism (MSB-CTrust), where trust tickets are used to resolve the heterogeneity issue of trust evaluation methods in different domains. Besides, MSB-CTrust provides embedded salary distribution rules, which are used to encourage all devices to behave honestly based on a revenue strategy game. Performance evaluations in the experiments show the feasibility and effectiveness of MSB-CTrust.

Generative Adversarial Learning for Trusted and Secure Clustering in Industrial Wireless Sensor Networks

Traditional machine learning techniques have been widely used to establish the trust management systems. However, the scale of training dataset can significantly affect the security performances of the systems, while it is a great challenge to detect malicious nodes due to the absence of labeled data regarding novel attacks. To address this issue, this paper presents a generative adversarial network (GAN) based trust management mechanism for Industrial Wireless Sensor Networks (IWSNs). First, type-2 fuzzy logic is adopted to evaluate the reputation of sensor nodes while alleviating the uncertainty problem. Then, trust vectors are collected to train a GAN-based codec structure, which is used for further malicious node detection. Moreover, to avoid normal nodes being isolated from the network permanently due to error detections, a GAN-based trust redemption model is constructed to enhance the resilience of trust management. Based on the latest detection results, a trust model update method is developed to adapt to the dynamic industrial environment. The proposed trust management mechanism is finally applied to secure clustering for reliable and real-time data transmission, and simulation results show that it achieves a high detection rate up to 96%, as well as a low false positive rate below 8%.

Blockchain‐enhanced trust management for mobile edge computing‐enabled intelligent vehicular collaboration in the 6G era

AbstractThe rapid development and increasing popularity of mobile devices have facilitated the development of computation‐intensive applications and services, but their constrained compute, storage and battery capabilities have also created the requirements for optimized resource provision and service orchestration. Emerging mobile edge computing (MEC) and next‐generation networks (6G) can meet these requirements of mobile devices by migrating complex computing tasks and large volumes of data to edge servers with acceptable latency, thus effectively enhancing the quality of experience (QoE) of mobile users. Intelligent Vehicular Collaboration, as an intelligent mobile application or service in intelligent connected vehicle (ICV) applications, enables precise and effective driving decisions based on a large amount of real‐time traffic information statistics and intelligent analysis of vehicle status to improve the user driving experience and the efficiency of intelligent transportation systems(ITS). However, the openness and dynamics of wireless communications and the high frequency of information interaction of MEC‐enabled intelligent vehicular collaboration services in the 6G era make them highly vulnerable to cyber attacks. How to achieve dynamically trustworthy, secure, and intelligent vehicular collaboration services in the presence of malicious node access and cyber attacks in the 6G MEC‐enabled vehicular network is a significant challenge. In this paper, we propose a novel blockchain‐enabled trust management mechanism to address this challenge. First, we design a spatiotemporal‐correlated message credibility assessment method and a dynamic mutual trust evaluation method to obtain accurate ratings for messages and mobile nodes respectively. Based on the above methods, we propose an improved blockchain‐based intelligent trust management mechanism that enables transparent, tamper‐evident, and trustworthy secure intelligent vehicular collaboration services while maintaining stable and efficient system performance. Simulation results show that our suggested solutions can efficiently evaluate the trust level of ICVs and achieve efficient and secure intelligent vehicular collaboration with acceptable communication and computation overheads.

DTM-CPS: Dynamic Trust Management Mechanism for Cyber Physical System based on Dirichlet Reputation System

DTM-CPS: Dynamic Trust Management Mechanism for Cyber Physical System based on Dirichlet Reputation System

Blockchain and digital twin empowered trustworthy self-healing for edge-AI enabled industrial Internet of things

The public has regarded Edge-AI enabled Industrial Internet of Things (IIoT) as the crucial foundation in the intelligent digital factories in Industry 4.0. It can fully catch the massive production data derived from the complex production process, and provide efficient, intelligent services. However, the deployment of edge AI aggravates the complexity and security risks caused by the massive heterogeneous resource-constrained and vulnerable edge IIoT devices. Effective fault prevention is crucial to ensure the security and robustness of the IIoT with numerous vulnerable edge devices. Most existing solutions are based on the history log, which can hardly defend against attacks and is easy to cause excessive maintenance. To address this issue, we propose a trustworthy self-healing scheme based on the combination of distributed digital twin (DT) and blockchain, to ensure the security and robustness of the industrial system network. We first propose an implementation architecture of the distributed DT based self-healing IIoT to apply the distributed DT simulation capability fully. In addition, we provide a DT simulation operating mechanism for the controlled industrial devices, considering the requirement of users and constrained resources of edge servers. Moreover, this work proposes a blockchain-based decentralized trust management mechanism to ensure the reliability of self-healing. The security analysis and performance evaluation show the security and efficiency of our proposal.

A Double-Layer Blockchain Based Trust Management Model for Secure Internet of Vehicles

The Internet of Vehicles (IoV) enables vehicles to share data that help vehicles perceive the surrounding environment. However, vehicles can spread false information to other IoV nodes; this incorrect information misleads vehicles and causes confusion in traffic, therefore, a vehicular trust model is needed to check the trustworthiness of the message. To eliminate the spread of false information and detect malicious nodes, we propose a double-layer blockchain trust management (DLBTM) mechanism to objectively and accurately evaluate the trustworthiness of vehicle messages. The double-layer blockchain consists of the vehicle blockchain and the RSU blockchain. We also quantify the evaluation behavior of vehicles to show the trust value of the vehicle’s historical behavior. Our DLBTM uses logistic regression to accurately compute the trust value of vehicles, and then predict the probability of vehicles providing satisfactory service to other nodes in the next stage. The simulation results show that our DLBTM can effectively identify malicious nodes, and over time, the system can recognize at least 90% of malicious nodes.

A decentralized trust management mechanism for crowdfunding

Crowdfunding is an act of raising funds from the public to support an individual or organizational project, and the success of crowdfunding projects lies in gaining backers' trust. In practice, however, crowdfunding platforms are rife with fraudulent crowdfunding projects. While some crowdfunding platforms use third-party auditors to verify projects before publishing, this also runs the risk of collusion between crowdfunders and auditors. The recent introduction of blockchain has increased the transparency of crowdfunding, but they, too, lack fraud prevention measures. Obviously, existing crowdfunding trust management schemes are not applicable under a zero-trust scenario. In this study, we propose a blockchain-based trust management mechanism for crowdfunding to address the fraudulent behavior in the existing crowdfunding systems. We introduce an auditor committee to audit crowdfunding projects credibly. A random selection algorithm on the blockchain enables the distributed generation of auditor committees with a trade-off between the randomness of selection and auditor expertise. The audit results are based on independent auditing by the audit committee members and are presented by the smart contract, avoiding the influence of third parties. In addition, we also provide an incentive mechanism to encourage auditors who perform accurate audits. Theoretical analyses and experiments show that our scheme can effectively deal with malicious behaviors and has an acceptable overhead.

A recommendation management defense mechanism based on trust model in underwater acoustic sensor networks

Underwater acoustic sensor networks (UASNs) have been applied in many civilian and military scenarios, but it is vulnerable to various security threats due to the broadcast transmission characteristics and the environment in which they are located. Trust management mechanism has been proven to be an effect way to improve network security. However, in the trust assessment process, some nodes may provide false suggestions, which will lead to trust conflicts and affect the normal operation of the trust mechanism. To screen out unreliable recommendations and dishonest nodes in the network and avoid potential dangers, a recommendation management trust mechanism based on collaborative filtering and variable weight fuzzy algorithm (CFFTM) is proposed in this paper. First, three kinds of evidence of trust: communication-based evidence, data-based evidence, and energy-based evidence are select as indicators. Then the variable weight fuzzy comprehensive evaluation algorithm is applied to calculate the direct trust value of the node. Secondly, in order to quantify the honesty ability of nodes, honesty degree is defined. Then the overall recommendation trust value of the node is obtained using the proposed collaborative filtering algorithm. The simulation results show that the mechanism can well filter out unreliable recommendations and improve the recognition rate and stability of the trust model under typical attack scenarios.

TRUCON: Blockchain-Based Trusted Data Sharing With Congestion Control in Internet of Vehicles

The Internet of vehicles (IoV) has a substantial impact on traffic efficiency improvement and accidents avoidance. Due to restricted resources, vehicles must share observed data with RSUs and other vehicles to execute some time-tolerant computing tasks. However, data provided by vehicles cannot always be trusted due to the presence of attackers. Fake messages could have catastrophic ramifications, such as vehicle collisions. Furthermore, extensive data sharing might cause channel congestion, resulting in the loss of vital messages during delivery. To overcome the aforementioned issues, we propose TRUCON, a blockchain-based trusted data sharing mechanism with congestion control in IoV. Firstly, we propose a Kademlia algorithm-based traffic data forwarding method to control channel congestion state. By adjusting the bucket size and distance threshold, source vehicles can limit the number of reference vehicles forwarded. Secondly, we present a cuckoo filter-based traffic data deduplication and discrimination approach. To avoid repetitive sharing, vehicles and RSUs can check their local filters to verify if the current data report has been shared. Based on the foregoing, we propose a blockchain-based trust management mechanism with congestion control. RSUs serve as full nodes while vehicles are light nodes in the blockchain. Finally, we develop a trust management prototype system with congestion control that incorporates both on-chain and off-chain parts. It signifies that our scheme is both feasible and effective.

FogTrust: Fog-Integrated Multi-Leveled Trust Management Mechanism for Internet of Things

The Internet of Things (IoT) is widely used to reduce human dependence. It is a network of interconnected smart devices with internet connectivity that can send and receive data. However, the rapid growth of IoT devices has raised security and privacy concerns, with the identification and removal of compromised and malicious nodes being a major challenge. To overcome this, a lightweight trust management mechanism called FogTrust is proposed. It has a multi-layer architecture that includes edge nodes, a trusted agent, and a fog layer. The trust agent acts as an intermediary authority, communicating with both IoT nodes and the fog layer for computation. This reduces the burden on nodes and ensures a trustworthy environment. The trust agent calculates the trust degree and transmits it to the fog layer, which uses encryption to maintain integrity. The encrypted value is shared with the trust agent for aggregation to improve the trust degree’s accuracy. The performance of the FogTrust approach was evaluated against various potential attacks, including On-off, Good-mouthing, and Bad-mouthing. The simulation results demonstrate that it effectively assigns low trust degrees to malicious nodes in different scenarios, even with varying percentages of malicious nodes in the network.