Trust Value Calculation Research Articles

Collaborative Filtering Recommender System for Online Learning Resources with Integrated Dynamic Time Weighting and Trust Value Calculation

Collaborative Filtering Recommender System for Online Learning Resources with Integrated Dynamic Time Weighting and Trust Value Calculation

Privacy-protecting predictive cache method based on blockchain and machine learning in Internet of vehicles

To solve the privacy leakage problem faced by Internet of Vehicles (IoV) users when enjoying location-based services (LBS), a privacy-protecting predictive cache method based on blockchain and machine learning (BML-PPPCM) is proposed. First, a Bi-directional Long-Short Term Memory (Bi-LSTM) model is used to predict query requests over a future period based on historical request information. The predicted results are recommended to neighbors and broadcast to requestors. Then, deep Q-learning (DQN) is utilized to determine the optimal cache decision. Finally, a trust mechanism is introduced to calculate trust values, and blockchain is used to store transaction data and trust data, preventing malicious tampering by attackers. The simulation results show that BML-PPPCM has a higher cache hit ratio than other similar schemes and performs well in privacy protection and suppression of malicious and incentive denial of service providers.

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

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

Access Control Strategy for the Internet of Vehicles Based on Blockchain and Edge Computing

Data stored in the Internet of Vehicles (IoV) face problems with ease of tampering, easy disclosure and single access control. Based on this problem, we propose an access control scheme for the IoV based on blockchain, trust values and weighted attribute-based encryption, called the Blockchain Trust and Weighted Attribute-Based Access Control Strategy (BTWACS). First, we utilize both local and global blockchains to jointly maintain the generation, verification and storage of blocks, achieving distributed data storage and ensuring that data cannot arbitrarily be tampered with. Local blockchain mainly uses Road Side Unit (RSU) technology to calculate trust values, while global blockchain is mainly responsible for data storage and access policy selection. Secondly, we design a blockchain-based trust evaluation scheme called Blockchain-Based Trust Evaluation (BBTE). In this evaluation scheme, the trust value of the vehicle node is based on four factors: initial trust, historical experience trust, recommendation trust and RSU observation trust. CRITIC is used to determine the optimal weights of four factors to obtain the trust value. Then, we use the Network Simulator version 3 (NS3) to verify the security and accuracy of BBTE, improving the recognition accuracy and detection rate of malicious vehicle nodes. Finally, by mining the association relationships between attribute permissions among various roles, we construct a hierarchical access control strategy based on weight and trust, and further optimize the access strategy through pruning techniques. The experiment results indicate that this scheme can effectively respond to gray hole attacks, defamation attacks and collusion attacks from other vehicle nodes. This method can effectively reduce the computing and transmission costs of vehicles and meet the access requirements of multiple entities and roles in the IoV.

A Cross-Domain Authentication and Authorization Model for New Power System Based on Trust Management

The access of new power service terminals brings about a new demand for cross-domain and cross-system security access. Therefore, this paper proposes a cross-domain authentication and authorization model for a new power system based on trust management, which is realized through the cooperation of certificate authority, authentication module, authorization module, and trust management module. Firstly, the model proposed in this paper includes a cross-domain authentication method, which realizes authentication between domains by issuing and verifying tokens for terminals with sufficient trust. Secondly, the model includes an authorization method based on attribute and trust value, which realizes the authorization of the terminal through attribute policy and trust evaluation. Finally, a trust value calculation method for terminal cross-domain access is included in the model. In this paper, the proposed model and method are simulated to demonstrate the correctness and effectiveness of the model. The experimental results show that the model has the advantages of universality, efficiency, and ease of management, and the terminal can access resources safely and efficiently across domains.

Framework for Data Trust using Block-Chain Technology and Adaptive Transaction Validation

Trust is the main barrier preventing widespread data sharing. The lack of transparent infrastructures for implementing data trust prevents many data owners from sharing their data and concerns data users regarding the quality of the shared data. Blockchain technology proposes a distributed and transparent administration by employing multiple parties to maintain consensus on an immutable ledger. This project presents an end-to-end framework for data trust to enhance trustworthy data sharing utilizing blockchain technology. We also suggest an adaptive solution to determine the number of transaction validators based on the computed trust value.

Formal Analysis of Trust and Reputation for Service Composition in IoT

The exponential growth in the number of smart devices connected to the Internet of Things (IoT) that are associated with various IoT-based smart applications and services, raises interoperability challenges. Service-oriented architecture for IoT (SOA-IoT) solutions has been introduced to deal with these interoperability challenges by integrating web services into sensor networks via IoT-optimized gateways to fill the gap between devices, networks, and access terminals. The main aim of service composition is to transform user requirements into a composite service execution. Different methods have been used to perform service composition, which has been classified as trust-based and non-trust-based. The existing studies in this field have reported that trust-based approaches outperform non-trust-based ones. Trust-based service composition approaches use the trust and reputation system as a brain to select appropriate service providers (SPs) for the service composition plan. The trust and reputation system computes each candidate SP's trust value and selects the SP with the highest trust value for the service composition plan. The trust system computes the trust value from the self-observation of the service requestor (SR) and other service consumers' (SCs) recommendations. Several experimental solutions have been proposed to deal with trust-based service composition in the IoT; however, a formal method for trust-based service composition in the IoT is lacking. In this study, we used the formal method for representing the components of trust-based service management in the IoT, by using higher-order logic (HOL) and verifying the different behaviors in the trust system and the trust value computation processes. Our findings showed that the presence of malicious nodes performing trust attacks leads to biased trust value computation, which results in inappropriate SP selection during the service composition. The formal analysis has given us a clear insight and complete understanding, which will assist in the development of a robust trust system.

TBMOR: A lightweight trust-based model for secure routing of opportunistic networks

Opportunistic networks are self-organizing dynamic networks that use the communication opportunities presented by node movement to enable data delivery. Due to the indirect nature of inter-node connections and dynamic topology, opportunistic networks are vulnerable to malicious attacks, which poses a great challenge to their security. In this paper, we first propose a trust-based model for the secure routing of opportunistic networks (TBMOR), which incorporates forwarding positive degree (FP) and node activity degree (NA) into the calculation of trust value, evaluates nodes in the network by a dynamic combination of direct trust and indirect trust, and then optimizes the next node list using trust values and pruning strategies. Secondly, this paper extends the network life cycle by dynamically setting up energy thresholds to reduce network redundancy by dynamically partitioning the number of replicas using trust values.Simulation results show that compared to other routers, TBMOR can effectively resist malicious attacks, has a higher message delivery rate and lower transmission latency than other routers.

TrustBlkSys: A Trusted and Blockchained Cybersecure System for IIoT

Industrial Internet of Things (IIoT) has emerged as a new paradigm in the era of smart systems where interactions and transmission of messages among various entities in an industrial ecosystem are done autonomously. This includes manufacturing of products, shipment process, storage of records, and counting the data, to name a few. However, the involvement of multiple cyber threats that accompany the smart devices may lead the organizations on a heavy risk of profits. Even though the number of cybersecurity approaches have been proposed to ensure a secure and efficient communication mechanism in IIoT systems, the identification of cybersecurity issues in IIoT applications while exchanging the data is still at its early stage. The aim of this article is to propose an efficient cybersecurity communication mechanism using the trust evaluation system of each device to make a correct decision during communication. In addition, the evaluated trust of each device is further analyzed and monitored through a blockchain-based mechanism. The proposed approach is validated against an existing scheme in terms of various security metrics, such as sensitivity of devices while communicating, convergence time of transmitting information and percentage, and probability of malicious devices in terms of data delivery rate, false authentication, and trust value computation.

Fuzzy User Access Trust Model for Cloud Access Control

Cloud computing belongs to a set of policies, protocols, technologies through which one can access shared resources such as storage, applications, networks, and services at relatively low cost. Despite the tremendous advantages of cloud computing, one big threat which must be taken care of is data security in the cloud. There are a dozen of threats that we are being exposed to while availing cloud services. Insufficient identity and access management, insecure interfaces and Applications interfaces (APIs), hijacking, advanced persistent threats, data threats, and many more are certain security issues with the cloud platform. APIs and service providers face a huge challenge to ensure the security and integrity of both network and data. To overcome these challenges access control mechanisms are employed. Traditional access control mechanisms fail to monitor the user operations on the cloud platform and are prone to attacks like IP spoofing and other attacks that impact the integrity of the data. For ensuring data integrity on cloud platforms, access control mechanisms should go beyond authentication, identification, and authorization. Thus, in this work, a trust-based access control mechanism is proposed that analyzes the data of the user behavior, network behavior, demand behavior, and security behavior for computing trust value before granting user access. The method that computes the final trust value makes use of the fuzzy logic algorithm. The trust value-based policies are defined for the access control mechanism and based on the trust value outcome the access control is granted or denied.

An Adaptive QoS and Trust-Based Lightweight Secure Routing Algorithm for WSNs

The limited resources and low computational power of wireless sensor networks (WSNs) make them vulnerable to various security attacks. Conventional security mechanisms require too many resources to allow the reliable operation of WSNs due to their resource-constrained nature. In addition, multihop communication in WSNs creates a requirement for guaranteed Quality of Service (QoS). Therefore, providing security while maintaining QoS and energy efficiency in WSNs are important design considerations. To further increase the performance of WSNs, there is a need to overcome the energy-hole problem, which leads to poor coverage of the field of interest. An energy-hole problem is created because of using poor deployment strategies. In this article, we define a multiobjective WSN optimization problem and present a novel algorithm known as lightweight secure routing (LSR) to manage WSNs that directly addresses the multiobjective WSN optimization problem. Our LSR algorithm uses ant colony optimization (ACO), an adaptive security model based on direct and indirect trust calculations, an adaptive QoS model, a hybrid deployment model based on 2-D Gaussian and uniform distributions, and an adaptive connectivity model that uses an appropriate communicational radius to ensure high connectivity between sensor nodes to solve the multiobjective WSN optimization problem. We divide our simulation results into three analyses, namely, trust model analysis, network scalability analysis, and security risk analysis to show that LSR outperforms the existing techniques in terms of energy consumed to calculate trust values, trust values convergence, network lifetime, average routing delay, and packet delivery ratio.

A Multi-Tier Trust-Based Security Mechanism for Vehicular Ad-Hoc Network Communications

Securing communications in vehicle ad hoc networks is crucial for operations. Messages exchanged in vehicle ad hoc network communications hold critical information such as road safety information, or road accident information and it is essential these packets reach their intended destination without any modification. A significant concern for vehicle ad hoc network communications is that malicious vehicles can intercept or modify messages before reaching their intended destination. This can hamper vehicle ad hoc network operations and create safety concerns. The multi-tier trust management system proposed in this paper addresses the concern of malicious vehicles in the vehicle ad hoc network using three security tiers. The first tier of the proposed system assigns vehicles in the vehicle ad hoc network a trust value based on behaviour such as processing delay, packet loss and prior vehicle behavioural history. This will be done by selecting vehicles as watchdogs to observe the behaviour of neighbouring vehicles and evaluate the trust value. The second tier is to protect the watchdogs, which is done by watchdogs' behaviour history. The third security tier is to protect the integrity of data used for trust value calculation. Results show that the proposed system is successful in identifying malicious vehicles in the VANET. It also improves the packet delivery ratio and end-to-end delay of the vehicle ad hoc network in the presence of malicious vehicles.

Effective and Enhanced Congestion Control Technique using Adaptive Routing Protocol to Improve the Performance of Crammed WSN

Nowadays, congestion on the network becomes a usual fact which is to be focused and to be addressed appropriately especially in Wireless Sensor Networks (WSN) for crammed type networks. Limited capacity on channel and wastage of energy are the root cause of congestion in WSN. The effects of congestion implies on QoS parameters, queue length, data arrival rate etc. Furthermore, data packets should be transmitted energy-efficiently to the sink node. In this regard, an Energy-Efficient Routing Protocol is offered to efficiently transmit the nodes to their end node or destination. To control congestion, an Adaptive Buffer trade-off and Improved Trust-based Energy Efficient Routing protocol are first presented, this method identifies the congestion free paths and the Buffer trade-off handles the buffer effectively. To route the protocol, a Cross-Layer Security-Based Fuzzy Logic Energy Efficient Packet Loss Preventive Routing Protocol has been developed. The proposed protocol routes the nodes and the protocol adopts a routing protocol that imparts security in terms of avoiding malicious nodes and preventing data loss. Consequently, to improve the lifetime of the network, a Density Aware Optimal Clustering Approach is presented. The proposed method is evaluated based on the Matlab software and the QoS performance metrics are Energy Consumption, Packet Delivery Ratio, Trust Value Computation, latency, reliability, energy efficiency, end-to end delay, Average Throughput, accuracy and network lifetime. The effectiveness of the research is evaluated by comparing it with other existing techniques, including Trust Aware Secure Routing Protocol (TASRP), Artificial Flora Algorithm Based Support Vector Machine (SVM-AF), Well-Organized Trust Estimation Based Routing Scheme (ETERS), Lion Fuzzy Bee, and Bat Fuzzy Bee Algorithm. Accordingly, the suggested method’s performance is higher than the existing methods for Packet delivery ratio, throughput, network lifetime, energy efficiency, and reliability. Consequently, the proposed method improves the congestion control performance in an energy-efficient manner, in future; a recently advanced technique is proposed to effectively improve the network performance respectively.

A secure trust-based protocol for hierarchical routing in wireless sensor network

<p><span>Wireless sensor networks (WSNs) became the backbone of the internet of things (IoT). IoT applications are vital and demand specific quality of service (QoS) requirements. In addition, security has become a primary concern to provide secure communication between wireless nodes, with additional challenges related to the node’s computational resources. Particular, the design of secure and resource efficient routing protocol is a critical issue in the current deployment of WSNs. Therefore, this paper proposes a novel secure-trust aware routing protocol (ST2A) that provides secure and reliable routing. The proposed protocol establishes communication routes based on calculated trust value in joint with a novel cluster head selection algorithm in the hierarchical routing process. The proposed trust-aware routing algorithm improves the routing security in WSN and optimizes many performance metrics related to WSNs unique characteristics. The results of simulation validate the feasibility of the proposed algorithm for enhancing the network lifetime up to 18% and data delivery by 17% as compared with some state-of-the-art routing algorithms.</span></p>

A hybrid trust computing approach for IoT using social similarity and machine learning.

Every year, millions of new devices are added to the Internet of things, which has both great benefits and serious security risks for user data privacy. It is the device owners’ responsibility to ensure that the ownership settings of Internet of things devices are maintained, allowing them to communicate with other user devices autonomously. The ultimate goal of the future Internet of Things is for it to be able to make decisions on its own, without the need for human intervention. Therefore, trust computing and prediction have become more vital in the processing and handling of data as well as in the delivery of services. In this paper, we compute trust in social IoT scenarios using a hybrid approach that combines a distributed computation technique and a global machine learning approach. The approach considers social similarity while assessing other users’ ratings and utilize a cloud-based architecture. Further, we propose a dynamic way to aggregate the different computed trust values. According to the results of the experimental work, it is shown that the proposed approaches outperform related work. Besides, it is shown that the use of machine learning provides slightly better performance than the computing model. Both proposed approaches were found successful in degrading malicious ratings without the need for more complex algorithms.

Cloud security based attack detection using transductive learning integrated with Hidden Markov Model

In recent years, organizations and enterprises put huge attention on their network security . The attackers were able to influence vulnerabilities for the configuration of the network through the network. Zero-day (0-day) is defined as vulnerable software or application that is either defined by the vendor or not patched by any vendor of organization. When zero-day attack is identified within the network there is no proper mechanism when observed. To mitigate challenges related to the zero-day attack, this paper presented HMM_TDL, a deep learning model for detection and prevention of attack in the cloud platform. The presented model is carried out in three phases like at first, Hidden Markov Model (HMM) is incorporated for the detection of attacks. With the derived HMM model, hyper alerts are transmitted to the database for attack prevention. In the second stage, a transductive deep learning model with k-medoids clustering is adopted for attack identification. With k-medoids clustering, soft labels are assigned for attack and data and update to the database. In the last phase, with computed HMM_TDL database is updated with computed trust value for attack prevention within the cloud.

A Malicious Intrusion Detection Model of Network Communication in Cloud Data Center

To enhance the efficiency of malicious intrusion detection of network communication, a malicious intrusion detection model for the network communication in cloud data center is designed. Firstly, the data preprocessing includes three parts: normal sample data modeling, standard data membership calculation and standard data membership calculation. Then, the characteristic value collection stage is completed. Finally, the intrusion detection classification and trust value calculation are completed to conclude the malicious intrusion detection of the network communication in cloud data center. Exploratory findings show that the malicious intrusion detection model for the network communication in cloud data center improves the intrusion detection rate, and reduces the detection time and false alarm rate.

A Data-Driven WSN Security Threat Analysis Model Based on Cognitive Computing

In this paper, we use cognitive computing to build a WSN security threat analysis model using a data-driven approach and conduct an in-depth and systematic study. In this paper, we develop a simulation platform (OMNeT++-based WSN Security Protocol Simulation Platform (WSPSim)) based on OMNeT++ to make up for the shortcomings of current WSN simulation platforms, improve the simulation capability of WSN security protocols, and provide a new technical means for designing and verifying security protocols. The WSPSim simulation platform is used to simulate and analyze typical WSN protocols and verify the effectiveness of the platform. In this paper, we mainly analyze the node malicious behavior by listening and judging the communication behavior of the nodes, and the current trust assessment is given by the security management nodes. When the security management node is rotated, its stored trust value is used as historical trust assessment and current trust assessment together to participate in the integrated trust value calculation, which improves the reliability of node trust assessment; to increase the security and reliability of the management node, a trust value factor and residual energy factor are introduced in the security management node election in the paper. According to the time of management node election, the weights of both are changed to optimize the election. Using the WSPSim simulation platform, a typical WSN protocol is simulated and analyzed to verify the effectiveness of the platform. In this paper, the simulation results of the LEACH protocol with an MD5 hash algorithm and trust evaluation mechanism and typical LEACH protocol as simulation samples are compared; i.e., the correctness of the simulation platform is verified, and it is shown that improving the security of the protocol and enhancing the security and energy efficiency of wireless sensor networks provide an effective solution.

ELM-NeuralWalk: trust evaluation for online social networks

Trust relationship plays an important role in online shopping, recommendation systems, internet of things, etc. The problem of trust evaluation among users in online social network (OSN) has attracted much attention, and has become a hot issue in the domain of social computing. However, the way of trust propagation and aggregation in OSN is still not clear, as well as the accuracy of trust calculation. In order to calculate the indirect trust, an ELM-NeuralWalk algorithm to implement trust propagation and aggregation is proposed. ELM-WalkNet firstly learns two-hop trust calculation rules, then calculates two-hop trust among users in the OSN. After that, ELM-NeuralWalk updates the OSN with the calculated trust value, so as to realise the calculation of multi-hop trust among users through iterative calling ELM-WalkNet. Unlike traditional solutions that use inference methods, ELM-WalkNet can learn trust calculation rules in an inductive way and accurately calculate indirect trust between users. Experiments on two real OSN datasets showed that ELM-NeuralWalk outperforms existing solutions.

Trust Management in Social Internet of Things (SIoT): A Survey

A survey on trust management in the Social Internet of Things (SIoT) is provided, beginning with a discussion of SIoT architectures and relationships. Using a variety of publication databases, we describe efforts that focus on various trust management aspects of SIoT. Trust management models comprise three themes: trust computation, aggregation, and updates. Our study presents a detailed discussion of all three steps. Trust computation and trust aggregation depend upon Trust Attributes (TAs) for the calculation of local and global trust values. Our paper discusses many strategies for aggregating trust, but “Weighted Sum” is the most frequently used in the relevant studies. Our paper addresses trust computation and aggregation scenarios. Our work classifies research by TAs (Social Trust, Quality of Service). We’ve categorized the research (reputation-based, recommendation-based, knowledge-based) depending on the types of feedback/opinions used to calculate trust values (global feedback/opinion, feedback from a friend, trustor’s own opinion considering the trustee’s information). Our work classifies studies (policy-based, prediction-based, weighted sum-based/weighted linear combination-based) by trust computation/aggregation approach. Two trust-update schemes are discussed: time-driven and event-driven schemes, while most trust management models utilize an event-driven scheme. Both trust computation and aggregation need propagating trust values in a centralized, decentralized, or semi-centralized way. Our study covers classifying research by trust updates and propagation techniques. Trust models should provide resiliency to SIoT attacks. This analysis classifies SIoT attacks as collaborative or individual. We also discuss scenarios depicted in the relevant studies to incorporate resistance against trust-related attacks in SIoT. Studies suggest context-based or context-free trust management strategies. Our study categorizes studies based on context-based or context-free approaches. To gain the benefits of an immutable, privacy-preserving approach, a future trust management system should utilize Blockchain technology to support non-repudiation and tracking of trust relationships.