Trust Management Mechanism Research Articles

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.

Toward security as a service: A trusted cloud service architecture with policy customization

With the rise of concerns over security and privacy in the cloud, the “security-on-demand” service mode dynamically provides cloud customers with trusted computing environments according to their specific security needs. Major challenges, however, remain to achieve this goal: (1) integrating an auditable, tamper-resistant trust-management mechanism into the cloud infrastructure and (2) building a protocol to guarantee the consistency of customers’ policies during virtual machine (VM) migrations. This study develops a new security-on-demand framework called a “policy-customized trusted cloud service” (PC-TCS) architecture that comprises two core components: an attribute-based signature (ABS)-based remote-attestation scheme to achieve trusted remote attestation with customized security policies and an ABS- and blockchain-based VM-migration protocol to support policy-customized trusted migration. To prove the availability of this architecture, we implemented a PC-TCS prototype based on Xen Hypervisor, the results of which indicate that (1) PC-TCS can be integrated into cloud infrastructure as part of a trusted computing base; (2) cloud users can customize the security policies of computing environments and validate their enforcement throughout the service life-cycle with the support of PC-TCS; and (3) PC-TCS can support policy-customized remote attestation and policy-customized migration with a minimal impact on performance.

A Novel Data Traceability Model Based on Blockchain and Digital Watermarking in Edge Computing

Most of the current data traceability models are centralized models, which cannot meet the traceability requirements of distributed edge nodes. Blockchain is a decentralized trust management mechanism based on the Internet. Its features that are difficult to tamper with and traceable provide a new solution to secure data traceability. However, many edge nodes have limited computing and storage resources, and cannot store large amounts of blockchain information. In order to solve the above problems, we propose a novel distributed traceability model based on digital watermarking and blockchain in edge computing. The model improves the security of the traditional traceability model by dividing the internal and external areas and electing the master nodes, reduces the storage capacity of resource-constrained nodes and solves the problem of poor scalability of the blockchain. The model can be applied to widely distributed edge node traceability scenarios, and can also trace the data in the case of watermark failure.

AgriTrust-A Trust Management Approach for Smart Agriculture in Cloud-based Internet of Agriculture Things.

Internet of Things (IoT) provides a diverse platform to automate things where smart agriculture is one of the most promising concepts in the field of Internet of Agriculture Things (IoAT). Due to the requirements of more processing power for computations and predictions, the concept of Cloud-based smart agriculture is proposed for autonomic systems. This is where digital innovation and technology helps to improve the quality of life in the area of urbanization expansion. For the integration of cloud in smart agriculture, the system is shown to have security and privacy challenges, and most significantly, the identification of malicious and compromised nodes along with a secure transmission of information between sensors, cloud, and base station (BS). The identification of malicious and compromised node among soil sensors communicating with the BS is a notable challenge in the BS to cloud communications. The trust management mechanism is proposed as one of the solutions providing a lightweight approach to identify these nodes. In this article, we have proposed a novel trust management mechanism to identify malicious and compromised nodes by utilizing trust parameters. The trust mechanism is an event-driven process that computes trust based on the pre-defined time interval and utilizes the previous trust degree to develop an absolute trust degree. The system also maintains the trust degree of a BS and cloud service providers using distinct approaches. We have also performed extensive simulations to evaluate the performance of the proposed mechanism against several potential attacks. In addition, this research helps to create friendlier environments and efficient agricultural productions for the migration of people to the cities.

NeuroTrust—Artificial-Neural-Network-Based Intelligent Trust Management Mechanism for Large-Scale Internet of Medical Things

Internet of Medical Things (IoMT) provides a diverse platform for healthcare to enhance the accuracy, reliability, and efficiency. In addition, it utilizes the productivity of available equipment to improve patients’ health. IoMT also provides distinct ways by which healthcare will be revolutionized as it provides numerous opportunities to handle operations with precision. However, numerous advantages have raised several security challenges, such as trust, data integrity, network constraints, and real-time processing among others. There is a requirement for a robust approach to maintain data integrity along with the behavior detection of nodes to completely maintain a secure environment. In the proposed approach, the mechanism is capable of maintaining a robust network by predicting and eliminating malicious nodes. The proposed NeuroTrust approach utilizes the trust parameters to evaluate the degree of trust that include reliability, compatibility, and packet delivery. This approach also lightens the two-way computation burden and uses a lightweight encryption mechanism to further enhance the security and integrity during data dissemination, which is required for the digital revolution in delivering efficient high quality healthcare. The performance of the proposed approach has been extensively evaluated against the absolute trust formulation, accuracy of trust computation, energy consumption, and several potential attacks. The simulation results show the effective performance to identify malicious and compromised nodes, and maintain resilience against various attacks.

A Dynamic Trust Evaluation and Update Mechanism Based on C4.5 Decision Tree in Underwater Wireless Sensor Networks

At present, Underwater Wireless Sensor Networks (UWSNs) have been widely used in enormous applications, and simultaneously face many security threats. The trust management mechanism plays an important role in protecting network security. Many theories, e.g., subjective logic, Bayesian, cloud model, entropy theory, evidence theory, etc., have been adopted to evaluate the node trust of wireless sensor networks. However, due to the unique characteristics of the underwater dynamic environment, the existing trust mechanisms used in traditional networks (such as P2P networks, Ad-hoc networks, etc.) cannot be directly used in UWSNs. Therefore, this paper proposes a new trust evaluation and update mechanism for underwater wireless sensor networks based on the C4.5 decision tree algorithm (TEUC). In the TEUC, trust evidences are first collected including data-based, link-based and node-based trust evidences. Then, the collected trust evidences are used to train the C4.5 decision tree. In addition, the reward and penalty factors are defined to update trust based on the sliding time window. Finally, simulation results demonstrate that the proposed algorithm outperforms the traditional ones in a dynamic network environment in terms of malicious node detection and energy consumption.

Blockchain-Based Secure Data Storage for Distributed Vehicular Networks

In this paper, a blockchain-based secure data sharing mechanism is proposed for Vehicular Networks (VNs). Edge service providers are introduced along with ordinary nodes to efficiently manage service provisioning. The edge service providers are placed in the neighborhood of the ordinary nodes to ensure smooth communication between them. The huge amount of data generated by smart vehicles is stored in a distributed file storage system, known as Interplanetary File System (IPFS). It is used to tackle the issues related to data storage in centralized architectures, such as data tampering, lack of privacy, vulnerability to hackers, etc. Monetary incentives are given to edge vehicle nodes to motivate them for accurate and timely service provisioning to ordinary nodes. In response, ordinary nodes give reviews to the edge nodes against the services provided by them, which are further stored in a blockchain to ensure integrity, security and transparency. Smart contracts are used to automate the system processes without the inclusion of an intermediate party and to check the reviews given to the edge nodes. To optimize gas consumption and to enhance the system performance, a Proof of Authority (PoA) consensus mechanism is used to validate the transactions. Moreover, a caching system is introduced at the edge nodes to store frequently used services. Furthermore, both security and privacy are enhanced in the proposed system by incorporating a symmetric key cryptographic mechanism. A trust management mechanism is also proposed in this work to calculate the nodes’ reputation values based upon their trust values. These values determine the authenticity of the nodes involved in the network. Eventually, it is concluded from the simulation results that the proposed system is efficient for VNs.

Trust in VANET: A Survey of Current Solutions and Future Research Opportunities

Security and privacy will play a pivotal role in the commercialization of Vehicular Ad-hoc NETworks (VANETs). Traditionally, both cryptographic and non-cryptographic approaches have been used by researchers to address security and privacy issues and achieve secure Intelligent Transportation System (ITS) applications. However, some security goals such as trust and reputation, are still hard to achieve through conventional cryptographic approaches. Trust is the degree of certainty with which the received information is accepted and acted upon. Historically trust has been computed for both the content generator and the content itself with former known as entity trust and the latter known as data trust. Both entity and content trust are equally important to support trustworthy communication in VANET. We review, analyze, and compare some of the recently proposed trust establishment and management mechanisms (from 2014 to 2019) in vehicular networks. Furthermore, we also discuss the weaknesses and inadequacies of existing trust establishment and management approaches when deployed in a VANET environment. Finally, we discuss some future challenges that will need to be addressed for trustworthy communications in vehicular networks.

Exploiting Social Relationships for Trustworthy D2D Relay in 5G Cellular Networks

The rapid growth of IoT is expected to offer a better quality of life and open up new business applications. However, the huge number of IoT devices are likely to create an immense burden on current wireless networks. Interestingly, next-generation 5G wireless networks envision the usage of mmWave frequencies for a manifold increase in data rates and user capacity. Nevertheless, the inherent LoS in mmWave communications raises significant new challenges in providing seamless coverage across the dense 5G networks. Emerging D2D communications are expected to offer a viable solution for this LoS challenge by providing relay services and extending the coverage. An NLoS device can use direct D2D communications for transmitting the data by using other LoS devices. The selection of relay requires maintaining a certain level of trust and privacy of the content. In this article, we explain that similar to human SN, personal trust among devices can be efficiently used to develop trustworthiness in D2D relay communications. By introducing a new distributed architecture for social relationships in D2D communications, we discuss and analyze new methods for a dynamic social relationship and trust management mechanism. Efficient and trustworthy social D2D relay networks significantly improve the capacity of mmWave 5G wireless networks. Simulation experiments corroborate our improvement in IoT device capacity and throughput. We also present coexistence of the proposed scheme with the existing state-of-the-art relay selection schemes.

A Reliable IoT Edge Computing Trust Management Mechanism for Smart Cities

With the development of the Internet of Things (IoT) technology, many end-users participate in the smart city through their own intelligent mobile devices (such as personal wearable devices, smartphones.) or sensors. The main challenge of the device sensing layer in the edge computing system of the IoT in the smart city is to select the trusted participants. Because not all the intelligent devices of the IoT are trustworthy, some intelligent devices of the IoT may maliciously damage the network or services and affect the service quality of the system. On this basis, an intelligent device selective recommendation mechanism based on the dynamic black-and-white list was proposed to solve the problem of selecting trusted participants to improve the service quality of the edge computing system of the IoT in the smart city. We introduced the evolutionary game theory to theoretically qualitatively study the validity and stability of the trust management mechanism proposed in this paper. The Lyapunov theory was used to prove the validity and stability of the trust management mechanism. The effectiveness of the trust management mechanism was verified by the actual scenario of the personal health monitoring management system and the air-quality monitoring and analysis system in the smart city environment. Experiments showed that the trust management mechanism proposed in this paper has a significant role in promoting the cooperation of multi intelligent devices in the IoT edge computing system. It more reliably resists the malicious attacks to service providers and is suitable for the large-scale IoT edge computing system in the smart city.

A Game-based Method for User Privacy Protection in Big Data

In order to protect the user privacy in the big data environment, We propose a security method based on game theory through trust management mechanism. The main contribution of this paper include:(1)analysis service game relationship in the big data environment, (2)combine trust evaluation with decision management, and apply game theory to design a user-centric scheme to protect privacy,(3)proposing a service selection method. By using game theory to restrict the service interaction between the users and services in big data, we introduce some factors corresponding game model. we then apply the trust evaluation and decision management to describe the relationship and trust quantify in which we propose a method for service selection to implement user privacy protection. Simulation results show that the proposed method is effective and can apply the dynamic game of incomplete information relationship between user and service to make decisions on the service selection, thus providing customized privacy protection.

Exploring the trust management mechanism in self-organizing complex network based on game theory

The trust management mechanism can used to establish cooperation relationship between nodes and decrease risk caused by the information interaction. In this paper, we propose a new trust management mechanism based on game theory to model the acquisition of indirect trust information between nodes, and put forward the corresponding punishment mechanism to enhance the cooperative behaviors. Moreover, single stage and repeated game model are established respectively to study the evolutionary stable strategy with different parameters, which illustrate the evolutionary selection process of node’s strategies in the indirect information assessment procedure. By the continuous adjustment of strategies, the whole network will converge to a stable state finally. The experiment results validate that the effectiveness and efficiency of the proposed model, which can promote the cooperate behavior with the neighbor nodes to obtain the maximum benefit when facing the indirect information assessment. Furthermore, the stability of the trust management system can also be evaluated and enhanced, which help us to design the effective trust management mechanism in the real world.

Blockchain-Based Trustworthy Edge Caching Scheme for Mobile Cyber-Physical System

To improve mobile users’ quality-of-experience (QoE) in the mobile cyber-physical system (MCPS), caching layered-coding contents on edge nodes that are close to mobile users has been advocated as a promising solution, which can efficiently lower the content delivery delay and mitigate the overhead of backhaul network. However, due to the complexity of trust management and the limited caching capacities of edge nodes, designing an efficient edge caching scheme for mobile users becomes a challenge. Meanwhile, the content caching in MCPS also faces some security problems, where edge nodes may return incorrect results or viruses to mobile users, and mobile users would deliberately refuse to pay for caching services. To tackle these problems, we propose a novel blockchain-based trustworthy edge caching scheme for mobile users in MCPS. Specifically, we first exploit blockchain to supervise the caching transactions between the edge nodes and mobile users in a distributed manner, whereby the caching service information cannot be modified and denied by any entities. Furthermore, we devise a trust management mechanism for mobile users to search the trustworthy caching services from diversified edge nodes, where the trust degree of the edge node is real-time evaluated and updated by mobile users based on the quality of caching service. To take full advantage of caching resources, we design a max–min-based resource allocation algorithm, with which the trustworthy edge node could fairly allocate its caching resource based on mobile users’ optimal demands. The simulation results show that the presented scheme not only improves the utilities of edge nodes but also increases the QoE of mobile users.

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.

An intelligent trust model for hybrid DDoS detection in software defined networks

SummarySoftware Defined Networks (SDNs) have been extensively studied in recent years. The centralised and programmable controller also brings many security challenges. As a conventional attack with the purpose of destruction, Distributed Denial of Service (DDoS) is still a threat for Software Defined Networks (SDNs). There is a lack of trust evaluation and management mechanism between the OpenFlow switches in SDNs. Therefore, in this paper, we propose a trust evaluation and management model, namely, the Intelligent Trust Model (ITM). In this framework, the Extreme Learning Machine (ELM) is applied to detect hybrid DDoS in Software Definition Networks (SDNs). Our model (ITM) can update the trust value of the OpenFlow switches in real time and respond quickly to different types of DDoS attacks. Compared with others, the experiment results show that our model can provide more efficient detection with high detection accuracy and low false positive rate for the hybrid DDoS attack. At last, in our proposal, OpenFlow switches with higher trust have a relatively higher priority. Therefore, we solve the flow conflict issue in the infrastructure layer.

Challenge-based collaborative intrusion detection networks under passive message fingerprint attack: A further analysis

Collaborative intrusion detection systems / networks (CIDSs/CIDNs) have been widely used, aiming to enhance the performance of a single intrusion detection system (IDS), by allowing an IDS node communicating and collecting information from others. To protect such collaborative systems against insider attacks, trust management mechanisms are often deployed to evaluate the trustworthiness of a node. In particular, challenge-based mechanism attempts to identify malicious nodes by measuring the deviation between challenges and responses. However, it is found that such mechanisms may be vulnerable to advanced insider attacks like Passive Message Fingerprint Attacks (PMFA), where malicious nodes can distinguish challenges by analyzing the sending strategy. In this paper, we further analyze the effectiveness of PMFA and investigate whether an improved sending strategy can help detect malicious nodes. Our study reveals that PMFA could still be valid under even an improved sending strategy, i.e., malicious nodes can hold its reputation level by understanding the network context. We then provide some insights on how to defeat such kind of attack by properly adjusting such mechanism.

Research on Docker Role Access Control Mechanism Based on DRBAC

With the rapid development of virtualization technology, security issues have also become increasingly prominent. All kinds of virtualization platforms use access control mechanisms to prevent illegal or legal entities from illegally accessing unauthorized resources.This paper discusses the access control mechanism adopted in Docker container, analyzes its shortcomings, and proposes a DRBAC based Docker role access control mechanism. This method is based on the role allocation method, dynamically expands the existing model, and adds a role access control mechanism to the Docker container. Through the distributed trust management and access control mechanism of DRBAC, we can solve the hidden danger brought by the main body because of delegated authority to other entities autonomously. When the system accesses Docker resources, this method can effectively control the access management and delegate problem of teh main body. In this way, we improve the access security of Docker container to host resources.

A Survey of Key Bootstrapping Protocols Based on Public Key Cryptography in the Internet of Things

The Internet of Things envisages connecting all physical objects or things to the Internet, using devices as diverse as smartphones, coffee makers, washing machines, automobiles, lamps, and wearable devices, among many others. The explosive growth of Internet-connected sensing and actuating devices has bridged the gap between the physical and the digital world, with new solutions bringing benefits to people, processes, and businesses. However, security will be a major challenge in enabling most of such applications. The lack of secure links exposes data exchanged by devices to theft and attacks, with hackers already showing a keen interest in this area. Secure communication in the IoT will require a multifaceted approach, in particular, targeting aspects as relevant as the communications' protocols and data that need to be secured. One of the major aspects among these is how keys are bootstrapped in devices, for the purpose of supporting secure communications. In this paper, we survey the state of the art in key bootstrapping protocols based on public-key cryptography in the Internet of Things. Due to its inherent scalability, such protocols are particularly relevant for the implementation of distributed identity and trust management mechanisms on the IoT, in the context of which devices may be authenticated and trusted. The reviewed proposals are analyzed and classified on the basis of the key delivery method, the underlying cryptographic primitive, and the authentication mechanism supported. We also identify and discuss the main challenges of implementing such methods in the context of IoT applications and devices, together with the main avenues for conducting further research in the area.

RobustTrust – A Pro-Privacy Robust Distributed Trust Management Mechanism for Internet of Things

In the promising time of the Internet, connected things have the ability to communicate and share information. The Internet of Things (IoT) cannot be implemented unless the security-related concerns have been resolved. Sharing information among different devices can compromise the private information of users. Thus, a suitable mechanism is needed to exclude the risk of malicious and compromised nodes. As follows, trust has been proposed in the literature as a useful technology to maintain users' security. Prior studies have proposed diverse trust management mechanisms to achieve adequate trust. The approach of cross-domain trust management is neglected that requires enormous considerations to address the difficulties related to cross-domain communication. In this paper, a cross-domain robust distributed trust management (RobustTrust) system is proposed, which makes a device fit for assessing trust towards different devices locally. In this system, the trust is divided into three components of security that help IoT nodes to become robust against compromised and malicious devices/nodes. The novelty of the proposed mechanism can be summarized in these aspects: A highly scalable trust mechanism, multiple components of evaluation to enhance robustness against attacks, and use of recommendations along with the feedback to build knowledge. Furthermore, the proposed mechanism is event-driven that helps nodes to evaluate trust more effectively as well as enhance the system efficiency. The proposed work is compared with the available trust evaluation schemes by concentrating on various attributes, such as trustworthiness, usability, and accuracy among others. The RobustTrust is validated by the extensive simulations considering absolute trust value's performance, the accuracy of trust estimation, and several potential attacks.

A Distributed Trust Management Mechanism for the Internet of Things Using a Multi-Service Approach

In the Internet of Things (IoT) heterogeneous devices can cooperate and communicate to provide or require determined services. A multi-service IoT is vulnerable to many types of malicious attacks. A trust management scheme is a strategy to establish trust between devices. In this work we propose a distributed trust management model for IoT using direct and indirect observations. The trust of a node is computed by the service quality and recommendations from neighbors. The nodes locally compute the trust of their neighbors, without the need of a central entity. We implemented the proposed strategy in the Cooja simulator of the Contiki operating system to analyze the performance of the trust model. We performed simulations using different number of malicious nodes performing the bad mouthing attack. Simulation results show the trust management scheme is able to detect malicious nodes in the network.