Proposed Trust Management Research Articles

3R: A reliable multi agent reinforcement learning based routing protocol for wireless medical sensor networks

Interest in the Wireless Medical Sensor Network (WMSN) is rapidly gaining attention thanks to recent advances in semiconductors and wireless communication. However, by virtue of the sensitive medical applications and the stringent resource constraints, there is a need to develop a routing protocol to fulfill WMSN requirements in terms of delivery reliability, attack resiliency, computational overhead, and energy efficiency. This paper proposes 3R, a reliable multi agent reinforcement learning routing protocol for WMSN. 3R uses a novel resource-conservative Reinforcement Learning (RL) model to reduce the computational overhead, along with two updating methods to speed up the algorithm convergence. The reward function is re-defined as a punishment, combining the proposed trust management system to defend against well-known dropping attacks. Furthermore, an energy model is integrated with the reward function to enhance the network lifetime and balance energy consumption across the network. The proposed energy model only uses local information to avoid the resource burdens and the security concerns of exchanging energy information. Experimental results prove the lightweightness, attacks resiliency and energy efficiency of 3R, making it a potential routing candidate for WMSN.

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-Enabled Trust Management Model for the Internet of Vehicles

The high-speed movement of nodes and the burstiness of interactions in the Internet of Vehicles pose huge challenges to the trusted vehicle collaboration and data sharing. Aiming at the disadvantages of existing authentication mechanisms and trust management models for connected vehicles, this paper proposes a trust management model enabled by blockchain to ensure the traceability, non-tampering, unforgeability, and transparency of vehicular interactions. The proposed trust management model leverages Dirichlet distribution, reputation regression, and revocation punishment to objectively and accurately reflect the trust status of vehicles. Simulation results on real-world datasets show that the proposed trust management model advantageously improves the accuracy of malicious vehicle detection and the attack resistance of connected vehicles.

Controversy-Adjudication-Based Trust Management Mechanism in the Internet of Underwater Things

Owing to the characteristics of underwater communication, such as limited bandwidth, low transmission speed, and long delivery delay, it is significantly challenging to address trust management in the Internet of Underwater Things (IoUT). In the process of trust calculation, trust judgments between nodes may be conflicting based on the obtained diverse trust evidences. However, in existing studies, the analysis of trust-conflict adjudication is nonexhaustive and lacking in detail. Therefore, a controversy-adjudication method is proposed in this study to handle conflict recommendations, and a novel trust management mechanism is further investigated based on the controversy-adjudication method, including three phases: 1) trust calculation; 2) trust recommendation; and 3) trust evaluation. First, trust evidences, e.g., packet delivery ratio, end-to-end packet transmission latency, and residual energy, are collected to calculate trust for trustees. In addition, for the trustor without sufficient trust evidences, recommendations are required and an incentive mechanism is proposed based on the prisoner’s dilemma to encourage neighbors to participate in trust recommendation. Finally, trust values of trustees are obtained by executing trust evaluation based on the controversy-adjudication mechanism. Simulation results demonstrate that the proposed trust management mechanism outperforms existing related works in terms of accuracy and robustness against unreliable IoUT.

An Intelligent Trust Cloud Management Method for Secure Clustering in 5G Enabled Internet of Medical Things

5G edge computing enabled Internet of Medical Things (IoMT) is an efficient technology to provide decentralized medical services while device-to-device (D2D) communication is a promising paradigm for future 5G networks. To assure secure and reliable communication in 5G edge computing and D2D enabled IoMT systems, this article presents an intelligent trust cloud management method. First, an active training mechanism is proposed to construct the standard trust clouds. Second, individual trust clouds of the IoMT devices can be established through fuzzy trust inferring and recommending. Third, a trust classification scheme is proposed to determine whether an IoMT device is malicious. Finally, a trust cloud update mechanism is presented to make the proposed trust management method adaptive and intelligent under an open wireless medium. Simulation results demonstrate that the proposed method can effectively address the trust uncertainty issue and improve the detection accuracy of malicious devices.

Building trust via blockchain in UAV‐assisted ultra‐dense 6G cellular networks

AbstractThe ultra‐dense deployment of unmanned aerial vehicles (UAVs) as mobile small cell base stations (SBS) is expected to support ultra‐high‐speed, ultra‐reliable, and ultra‐low‐latency wireless connections in sixth generation (6G) wireless cellular networks. Inevitably, the explosively increasing number of SBSs each with ever‐shrinking cell size will result in the spectrum scarcity and pose critical challenges to the spectrum management. Towards the centralized spectrum management system, a third‐party authority is employed to coordinate the spectrum sensing, sharing, and allocation among participants. However, a centralized authority is vulnerable to numerous security threats, such as single point of failure, denial of service attacks, and privacy disclosure, and cannot guarantee fair and efficient spectrum management without mutual trusts among participants. To address these problems, a novel framework of blockchain‐aided spectrum management is designed to securely record the spectrum auction data and spectrum allocation data in the decentralized spectrum sharing. Moreover, a trust management scheme is proposed to evaluate the trusts of all UAVs participating in the decentralized spectrum sharing, where the trust increase depends on legal use of spectrum allocation and honest report of spectrum sensing. In particular, it is shown that the proposed trust management can not only incentivize the UAVs to comply with the legal rule of spectrum sharing in blockchain, but also punish the malicious behaviour that either violates the spectrum sharing rule or provides misleading spectrum sensing results.

A Trust Management Method Against Abnormal Behavior of Industrial Control Networks Under Active Defense Architecture

Trusted computing is a typical active defense technology. Trust management is a core support technology of trusted computing. However, when trust management is applied in the industrial control systems, how to identify malicious behavior effectively, model trust relationships, and make a decision based on behavior trustworthiness, meanwhile how to ensure deployed trust mechanism does not affect the control network’s availability, is a significant issue that has not been solved in the previous literature. This paper proposes a trust management method against abnormal behavior of industrial control networks under active defense architecture. Firstly, we review the difficulties of trust management when applied to industrial control networks and analyze abnormal behaviors of the control operations under unknown threats. Then we extract trust information, model the trust relationship of abnormal behaviors, and establish a trust update and decision-making mechanism under the availability constraints of industrial control networks. Furthermore, we provide a deployment method of the proposed trust management in a distributed control network. Finally, we take five typical abnormal operations on control instruction in an industrial control network as an example and perform a detailed analysis and experimental verification of the proposed method. The results prove that the proposed trust management method has good immunity to abnormal behaviors of the control flow and can be deployed in an industrial control system with availability constraints.

K‐means clustering based energy and trust management routing algorithm for mobile ad‐hoc networks

SummaryMANETs (Mobile Ad‐Hoc Networks) have limited energy and are unable to energize themselves. This work aims to create a power management strategy for the MANET that saves energy. While routing based on the cluster, the cluster heads may fail or work improperly by the cause of power issues. As a result, the cluster heads experience instability when gathering information and interacting. The main focus of this research is identifying the unstable cluster heads and replacing them with other nodes that implement the self‐configurable cluster method. For defining the cluster heads successfully, we presented a k‐means protocol approach. The proposed k‐means technique identifies the abnormal or unnecessary variations of the cluster heads and the change in cluster count. A node is designated as a cluster head (CH) in MANET after the cluster value remains lesser than the existing routing threshold value. Information gathering, computing trust levels, and trust‐based configuration are the three key elements of the proposed trust management system, which communicates with each node to establish secure network connections. Finally, the testing results show that the proposed approach uses less energy and effectively manages trust in MANET.

Privacy‐preserving blockchain‐based authentication and trust management in VANETs

Vehicular Ad Hoc Networks (VANETs) are characterized by high mobility of nodes and volatility, which make privacy, trust management, and security challenging issues in VANETs' design. In such networks, data can be exposed to a variety of attacks, the most dangerous is false information dissemination, which threatens the safety and efficiency of transportation systems. False emergency messages can be injected by inside attackers to announce fake incidents such as traffic accidents, resulting in a false information attack. As the data in VANET is based on events, any trust mechanism must first identify the true events. To address these security challenges, a blockchain-based authentication scheme and trust management model are proposed for VANETs. Using the authentication scheme, vehicles are enabled to send messages anonymously to the roadside units (RSUs) and the identity privacy of vehicles is protected. Besides, the proposed trust management model is designed to detect and deal with false information by evaluating the trustworthiness of vehicles and data. Using the trust model, when vehicles report an incident to the nearest RSU, the RSU is able to verify whether or not the incident took place. This mechanism ensures that RSUs send only verified event notifications. Finally, RSUs participate in updating the trust values of vehicles and store these values in the blockchain. The efficiency of the proposed authentication scheme is validated through analysis while the trust model is validated through simulations. The results obtained show that the proposed authentication scheme and the trust model provide better performance than other state-of-the-art models where malicious vehicles can be identified efficiently and RSUs are enabled to broadcast only legitimate events.

Trust Management in Industrial Internet of Things using a Trusted E-Lithe Protocol

The IoT has gained significant recognition from research and industrial communities over the last decade. The concept of Industrial IoT (IIoT) has emerged to improve industrial processes and reduce downtime or breach in secure communication. If automated, industrial applications can make the implementation process more convenient, it also helps increase productivity, but an external attacker may cause distortion to the process, which could cause much damage. Thus, a trust management technique is proposed for securing IIoT. The transition of the Internet to IoT and for industrial applications to IIoT leads to numerous changes in the communication processes. This transition was initiated by wireless sensor networks that have unattended wireless topologies and were comprised due to the nature of their resource-constrained nodes. In order to protect the sensitivity of transmitted information, the security protocol uses the Datagram Transport Layer Security (DTLS) mandated by Secure Constrained Application Protocol (CoAP). However, DTLS was designed for powerful devices and needed strong support for industrial applications connected through high-bandwidth links. In the proposed trust management system, machine learning algorithms are used with an elastic slide window to handle bigger data and reduce the strain of massive communication. The proposed method detected on and off attacks on nodes, malicious nodes, healthy nodes, and broken nodes. This identification is necessary to check if a particular node could be trusted or not. The proposed technique successfully predicted 97% of nodes' behavior faster than other machine learning algorithms.

Hierarchical Hybrid Trust Management Scheme in SDN-Enabled VANETs

One of the principal missions of security in the Internet of Vehicles (IoV) is to establish credible social relationships. The trust management system has been proved to be an effective security solution in a connected vehicle environment. The use of trust management can play a significant role in achieving reliable data collection and dissemination and enhanced user security in the Internet of Vehicles. However, due to a large number of vehicles, the limited computing power of individuals, and the highly dynamic nature of the network, a universal and flexible architecture is required to realize the trust of vehicles in a dynamic environment. The existing solutions for trust management cannot be directly applied to the Internet of Vehicles. To ensure the safe transmission of data between vehicles and overcome the problems of high communication delay and low recognition rate of malicious nodes in the current trust management scheme, an efficient flow forwarding mechanism of the RSU close to the controller in the Software-Defined Vehicular Network is used to establish a hierarchical hybrid trust management architecture. This method evaluates the dynamic trust change of vehicle behavior based on the trust between vehicles and the auxiliary trust management of the infrastructure to the vehicle, combined with static and dynamic information and other indicators. The proposed trust management scheme is superior to the comparative schemes in resisting simple attacks, selective misbehavior attacks, and time-dependent attacks under the condition of ensuring superior real-time performance. Its overall accuracy is higher than that of the baseline scheme.

Service Versus Protection: A Bayesian Learning Approach for Trust Provisioning in Edge of Things Environment

Edge of Things (EoT) technology enables end-users participation with smart sensors and mobile devices (such as smartphones and wearable devices) to the smart devices across the smart city. Trust management is the main challenge in EoT infrastructure to consider the trusted participants. The Quality of Service (QoS) is highly affected by malicious users with fake or altered data. In this article, a robust trust management (RTM) scheme is designed based on Bayesian learning and collaboration filtering. The proposed RTM model is regularly updated after a specific interval with the significant decay value to the current calculated scores to update the behavior changes quickly. The dynamic characteristics of edge nodes are analyzed with the new probability score mechanism from recent services’ behavior. The performance of the proposed trust management scheme is evaluated in a simulated environment. The percentage of collaboration devices is tuned as 10%, 50%, and 100%. The maximum accuracy of 99.8% is achieved from the proposed RTM scheme. The experimental results demonstrate that the RTM scheme shows better performance than the existing techniques in filtering malicious behavior and accuracy.

A hybrid trust management framework for a multi-service social IoT network

Social IoT (or SIoT) is an alternate architectural pattern for IoT, which involves IoT devices with social behavioural attributes. A SIoT-based service network makes use of social collaboration between IoT devices to enable low-latency collaborative services and applications. A key challenge in implementing a SIoT-based service network in a multi-vendor environment of heterogeneous devices is the issue of Trust. In this paper, we propose a hybrid trust management framework that makes use of Probabilistic Neighbourhood Overlap (P-NO), a method for estimating tie-strengths between nodes. The neighbourhood overlap concept is borrowed from past research in sociology and extended in our paper for directed social networks. Our proposed trust management framework is hybrid because: (1) P-NO is applied on a social graph that is generated from two types of social networks — the IoT device owners’ online social network (like Facebook) and the IoT-devices’ social network (i.e. the SIoT network). Accordingly, the approach makes use of both human intelligence and device artificial intelligence for trust management. (2) The framework uses a mix of dynamic (interaction-based) and static (graph-based) approach for trust management. It helps in limiting resource overheads of a pure dynamic approach, while still benefiting from its higher accuracy compared to a pure-static approach. We provide both theoretical and simulation-based analysis of our trust management framework. Our study shows the effectiveness of the proposed framework in handling different attack scenarios while requiring limited storage and computational resources in IoT devices.

Named Data Networking‐Based On‐Demand Secure Vehicle‐To‐Vehicle Communications

The detection of secure vehicles for content placement in vehicle to vehicle (V2V) communications makes a challenging situation for a well‐organized dynamic nature of vehicular ad hoc networks (VANET). With the increase in the demand of efficient and adoptable content delivery, information‐centric networking (ICN) can be a promising solution for the future needs of the network. ICN provides a direct retrieval of content through its unique name, which is independent of locations. It also performs better in content retrieval with its in‐network caching and named‐based routing capabilities. Since vehicles are mobile devices, it is very crucial to select a caching node, which is secure and reliable. The security of data is quite important in the vehicular named data networking (VNDN) environment due to its vital importance in saving the life of drivers and pedestrians. To overcome the issue of security and reduce network load in addition to detect a malicious activity, we define a blockchain‐based distributive trust model to achieve security, trust, and privacy of the communicating entities in VNDN, named secure vehicle communication caching (SVC‐caching) mechanism for the placement of on‐demand data. The proposed trust management mechanism is decentralized in nature, which is used to select a trustworthy node for cluster‐based V2V communications in the VNDN environment. The SVC‐caching strategy is simulated in the NS‐2 simulator. The results are evaluated based on one‐hop count, delivery ratio, cache hit ratio, and malicious node detection. The results demonstrate that the proposed technique improves the performance based on the selected parameters.

A trust management model based on NSGA-II in mobile grid system

Mobile Grid network connects large number of mobile devices like smartphones, tablets, PDAs, wireless digital medical equipment’s etc for the purpose of sharing their resources and performing the task collaboratively and cooperatively. The mobile nodes participating in the mobile grid are autonomous and open in nature making them more vulnerable to data and control attacks made by malicious or selfish nodes. Preventing these malicious or selfish nodes and identifying the trusted nodes to participate in the network is an NP-hard problem. To recognize trusted nodes in the mobile grid system a novel trust management model is proposed in this paper by applying an elitist multi objective optimization algorithm Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The proposed trust management model assesses the trust index of each mobile node in the network using various evaluation factors or attributes and then obtains the non-dominated set of trusted nodes in each front. Comparative analysis of the proposed trust model shows that the proposed model can be a potential candidate for implementing trust management in mobile grid network.

Combined Preference Ranking Algorithm for Comparing and Initial Ranking of Cloud Services

Background: With the immense significance of cloud computing over the decade, different IT companies offer varieties of cloud services. Objective: The selection of cloud services from the expanding range of Cloud Service Providers (CSPs) makes it difficult for the Cloud Consumers (CCs) to choose a CSP based on their preferences. Methods: In this context, this paper proposes an efficient trust management architecture for cloud service selection and put forward Combined Preference Ranking Algorithm (CPRA) for initial ranking of CSPs and their services before doing any transaction in the past based on CCs requirements. Results: The proposed trust management architecture prompts the CSPs to improve the Quality of Service (QoS) by adhering to Service Level Agreement (SLA). Conclusion: The experimental results show that compared with other ranking approaches CPRA generates the accurate ranking list of CSPs with minimal execution time.

A Matrix Factorization Model for Hellinger-Based Trust Management in Social Internet of Things

The Social Internet of Things (SIoT), integration of the Internet of Things and Social Networks paradigms, has been introduced to build a network of smart nodes that are capable of establishing social links. In order to deal with misbehaving service provider nodes, service requestor nodes must evaluate their trustworthiness levels. In this paper, we propose a novel trust management mechanism in the SIoT to predict the most reliable service providers for each service requestor, which leads to reduce the risk of being exposed to malicious nodes. We model the SIoT with a flexible bipartite graph (containing two sets of nodes: service providers and service requestors), then build a social network among the service requestor nodes, using the Hellinger distance. Afterward, we develop a social trust model using nodes' centrality and similarity measures to extract trust behaviors among the social network nodes. Finally, a matrix factorization technique is designed to extract latent features of SIoT nodes, find trustworthy nodes, and mitigate the data sparsity and cold start problems. We analyze the effect of parameters in the proposed trust prediction mechanism on prediction accuracy. The results indicate that feedbacks from the neighboring nodes of a specific service requestor with high Hellinger similarity in our mechanism outperforms the best existing methods. We also show that utilizing the social trust model, which only considers a similarity measure, significantly improves the accuracy of the prediction mechanism. Furthermore, we evaluate the effectiveness of the proposed trust management system through a real-world SIoT use case. Our results demonstrate that the proposed mechanism is resilient to different types of network attacks, and it can accurately find the most proper and trustworthy service provider.

A Fuzzy Lattice System to Trust Management in Mobile Grid

Mobile Grid is a crossbreed technology formed by amalgamation of the two prominent technologies namely mobile technology and grid technology that enable sharing and collaboration of mobile resources cooperatively, transparently, efficiently, reliably and securely. Mobile Grid considers the mobility issues and overcomes the constraints and deficiencies in both the technologies. However, this heterogeneous, dynamic and open mobile grid network is more prone to malicious and selfish nodes inside and outside the network. Hence, a vigorous security mechanism is needed that considers different security threats and pro-vide different levels of security services. Here, we propose one such preventive security service based on Trust Management. The proposed trust management service uses a novel fuzzy lattice approach for trust estimation of the nodes in the network. A node with high trust value is allowed to participate in the network. A malicious node having low trust value is prevented from performing the task. A fuzzy lattice approach can compute incrementally the same intervals in the training data independent of the order of presentation within a short period. Experimental analysis of the fuzzy lattice approach shows that the proposed approach outperforms most of the existing approaches based on fuzzy logic.

Consortium Blockchain-Based Secure Software Defined Vehicular Network

The vehicular ad hoc network (VANET) is a promising technology that can provide Internet access services for vehicles. With the development of VANET, tremendous intelligent vehicles will emerge mass and different communication requirements. Software-defined networking (SDN) is regarded as a potential technology to enhance network performance. In recent years, a new networking paradigm called software defined vehicular networks (SDVN) has been proposed. Nevertheless, the security issues still need to be considered for SDVN, because malicious vehicles can put forward fake requirements on the control plane of SDVN, which deteriorates the network performance in a certain degree. In this paper, we associate resources allocation problem with trust value of vehicles for the first time. The trust value of vehicles can be obtained through trust management system. Considering that there are many defects in the state-in-art trust management schemes, in this paper, a decentralized trust management architecture is designed which constitutes of three layers based on consortium blockchain. A joint proof-of-stake and modified practical Byzantine fault tolerance (PoS-mPBFT) algorithm is proposed to the shorten the confirmation time, which is deployed on RSUs. Different from previous researches that focus on designing methods to evaluate trust value, we use prediction model to estimate trust value of vehicles in the next period. After calculating trust value of vehicles, it assigns more resources to those high credibility vehicles when SDN services are provided. Meanwhile, to increase the efficiency of resource allocation, we convert the multiple-path mapping problem of the virtual network into the multi-commodity flow problem, which is solved by a heuristic algorithm. The simulation results indicate that the proposed trust management architecture and heuristic algorithm could provide better safety in SDVN and shorten consensus time, meanwhile effectively abstract underlying resources to enhance network load balance and capacity.

A Hierarchical Trust Model for Peer-to-Peer Networks

Trust has become an increasingly important issue given society’s growing reliance on electronic transactions. Peer-to-peer (P2P) networks are among the main electronic transaction environments affected by trust issues due to the freedom and anonymity of peers (users) and the inherent openness of these networks. A malicious peer can easily join a P2P network and abuse its peers and resources, resulting in a large-scale failure that might shut down the entire network. Therefore, a plethora of researchers have proposed trust management systems to mitigate the impact of the problem. However, due to the problem’s scale and complexity, more research is necessary. The algorithm proposed here, HierarchTrust, attempts to create a more reliable environment in which the selection of a peer provider of a file or other resource is based on several trust values represented in hierarchical form. The values at the top of the hierarchical form are more trusted than those at the lower end of the hierarchy. Trust, in HierarchTrust, is generally calculated based on the standard deviation. Evaluation via simulation showed that HierarchTrust produced a better success rate than the well-established EigenTrust algorithm.