Trust Management Techniques Research Articles

A Policy-Hiding Attribute-Based Access Control Scheme in Decentralized Trust Management

Internet of Medical Things (IoMT) technologies significantly improve the quality of health care, especially at the time when COVID-19 is becoming a worldwide pandemic. Due to the complexity of devices and user nodes in the IoMT system, there should be some ways to ensure the security and quality of the service or information. Decentralized trust management techniques are efficient means of promoting application security and reliability in these cases. However, the majority of currently utilized access control schemes cannot be applied in decentralized trust management systems or perform poorly owing to the numerous restrictions of decentralized systems. In this paper, we present a policy-hiding and multi-authority key generation CP-ABE scheme (PM-CPABE) for decentralized trust management systems, which could provide fine-grained access control capabilities. Meanwhile, the proposed scheme does not require any fully trusted entity, thus it can be well adapted to decentralized trust management systems. The scheme also implements policy hiding to protect user privacy. In addition, it supports large universe and outsourced decryption. The security analyses and performance comparisons give evidence of our scheme is security and efficient.

A detailed study on trust management techniques for security and privacy in IoT: challenges, trends, and research directions

A detailed study on trust management techniques for security and privacy in IoT: challenges, trends, and research directions

A Dynamic Trust-Related Attack Detection Model for IoT Devices and Services Based on the Deep Long Short-Term Memory Technique.

The integration of the cloud and Internet of Things (IoT) technology has resulted in a significant rise in futuristic technology that ensures the long-term development of IoT applications, such as intelligent transportation, smart cities, smart healthcare, and other applications. The explosive growth of these technologies has contributed to a significant rise in threats with catastrophic and severe consequences. These consequences affect IoT adoption for both users and industry owners. Trust-based attacks are the primary selected weapon for malicious purposes in the IoT context, either through leveraging established vulnerabilities to act as trusted devices or by utilizing specific features of emerging technologies (i.e., heterogeneity, dynamic nature, and a large number of linked objects). Consequently, developing more efficient trust management techniques for IoT services has become urgent in this community. Trust management is regarded as a viable solution for IoT trust problems. Such a solution has been used in the last few years to improve security, aid decision-making processes, detect suspicious behavior, isolate suspicious objects, and redirect functionality to trusted zones. However, these solutions remain ineffective when dealing with large amounts of data and constantly changing behaviors. As a result, this paper proposes a dynamic trust-related attack detection model for IoT devices and services based on the deep long short-term memory (LSTM) technique. The proposed model aims to identify the untrusted entities in IoT services and isolate untrusted devices. The effectiveness of the proposed model is evaluated using different data samples with different sizes. The experimental results showed that the proposed model obtained a 99.87% and 99.76% accuracy and F-measure, respectively, in the normal situation, without considering trust-related attacks. Furthermore, the model effectively detected trust-related attacks, achieving a 99.28% and 99.28% accuracy and F-measure, respectively.

A trust management model in internet of vehicles

The Internet of Things (IoT) is one of the most evolving technologies, which has a major impact on our daily life. Almost all new devices will have a feature to be connected and controlled over the Internet. Several applications are utilizing IoT to enhance routine processes and actions efficiently. The Internet of Vehicles (IoV) evolved from IoT, where vehicles communicate with each other or with other objects to have a better transportation environment to reduce the number of accidents and save people’s lives. IoV is considered new fields that need security requirements including confidentiality, integrity, availability, authentication, and trust. Trust management technique is used to validate entities behaviors automatically against well-defined policies. The major categories of trust model in IoV are based on entity, data, or a combination of both. This paper proposes a trust model which is based on a combination of entity and data to define the trust of vehicles and utilize the public key infrastructure to distribute certificates to vehicles. Based on certificate validation, messages will be trusted and accepted. This model has been tested across different simulation scenarios which showed that the proposed model detected malicious vehicles and trusted vehicles did not accept their messages.

Exploring Trust Modeling and Management Techniques in the Context of Distributed Wireless Networks: A Literature Review

Exploring Trust Modeling and Management Techniques in the Context of Distributed Wireless Networks: A Literature Review

An Efficient Trust Management Technique Using ID3 Algorithm With Blockchain in Smart Buildings IoT

Because of the rising population density, relationships are necessary to raise living standards through sending and receiving a wide range of services. Because of this, many means of object communication—regardless of their nature—are necessary to meet our daily needs. IoT is a network of physical things integrated with sensors, and software to communicate with each other. To establish a good connection, every object considered to be an associate of another object should meet certain requirements including scalability, interoperability, and trustworthiness. IoT security is a challenging task to protect the hardware and networks in the IoT system and a significant constraint to the deployment and realization of IoT. IoT security may include data confidentiality, authentication, access control, anonymity, and trust among services and products. Exchanging trust information is critical for assessing an entity’s trustworthiness. Therefore, trust information must be shared and stored securely to ensure reliability, honesty, and safety. We propose a secure trust management scheme built on blockchain technologies to secure the entire system in transparency, traceability, and material integrity. We implement a blockchain-based trust management architecture for smart buildings that collect node trust proof. It assigns a trust score to each node, securely stores them in an array, then the threshold value is computed using the ID3 Algorithm. IoT threshold value is broadcasted into the blockchain network and stored in the trusted list. According to the findings, our approach encompasses security measures such as tamper-proofing, attack resistance, reliability, and low functionality for IoT in smart buildings.

Enhanced Energy Efficient with a Trust Aware in MANET for Real-Time Applications

Mobile ad hoc networks (MANETs) are subjected to attack detection for transmitting and creating new messages or existing message modifications. The attacker on another node evaluates the forging activity in the message directly or indirectly. Every node sends short packets in a MANET environment with its identifier, location on the map, and time through beacons. The attackers on the network broadcast the warning message using faked coordinates, providing the appearance of a network collision. Similarly, MANET degrades the channel utilization performance. Performance highly affects network performance through security algorithms. This paper developed a trust management technique called Enhanced Beacon Trust Management with Hybrid Optimization (EBTM-Hyopt) for efficient cluster head selection and malicious node detection. It tries to build trust among connected nodes and may improve security by requiring every participating node to develop and distribute genuine, accurate, and trustworthy material across the network. Specifically, optimized cluster head election is done periodically to reduce and balance the energy consumption to improve the lifetime network. The cluster head election optimization is based on hybridizing Particle Swarm Optimization (PSO) and Gravitational Search Optimization Algorithm (GSOA) concepts to enable and ensure reliable routing. Simulation results show that the proposed EBTM-HYOPT outperforms the state-of-the-art trust model in terms of 297.99 kbps of throughput, 46.34% of PDR, 13% of energy consumption, 165.6 kbps of packet loss, 67.49% of end-to-end delay, and 16.34% of packet length.

Resisting bad mouth attack in vehicular platoon using node-centric weight-based trust management algorithm (NC-WTM)

Individuals use Vehicular Ad Hoc Networks (VANETs) to secure, reliable communication networks as a viable route of transmission. Platooning uses Inter-Vehicular Communication to supervise a group of vehicles (IVC). The information communicated, especially acceleration, location, and speed, can then be used to respond by longitudinal control legislation. Platoons require help between those vehicles to improve fuel efficiency, security, and various crises associated with driver errors. Despite these advances, the communication network must govern vehicles unprotected from adequate threat vectors, which malicious attacks might exploit. Considering that connected cars between trusts could increase security, whereas all active automobiles can develop and spread legitimate, accurate, and recognized information within the network. So, considering badmouth, this work proposes a unique node centric weight-based trust management algorithm (NC-WTM). By quickly recognising and deleting fraudulent vehicles and their generated messages, the NC-WTM (node-centric weighted trust management) technique improves aggregate platoon security. The NC-WTM beats the robust and privacy-preserving reputation management scheme (RPRep) and the attack resistant trust management scheme (ART) trust designs regarding accuracy, recall, and F-score. Metrics, such as precision results of 78%, recall of 69.3%, F-score of 60.4%, and accuracy of 89%, are achieved and optimised in this trust model. Abbreviations: VANETs: vehicular ad hoc networks; IVC: inter-vehicular communication; NC-WTM: node-centric weight-based trust management algorithm; WTM: weight-based trust management algorithm; RPRep: robust and privacy-preserving reputation management scheme; ART: attack-resistant trust management scheme; MANET: mobile ad hoc network; DSRC: dedicated short-range communication; WAVE: wireless access in vehicular environment; IVC: inter-vehicular communication; I2V: infrastructure-to-vehicle; V2I: vehicle-to-infrastructure; V2V: vehicle-to-vehicle; TA: trust authority; RSU: road side unit; OBU: on-board unit; GPS: global positioning system; WSN: wireless sensor network; VASNETs: vehicular sensor networks; CCW: cooperative collision warning; BMA: bad mouth attack; TDMA: time division multiple access; GDVAN: greedy detection for VANETs; SMTS: spider monkey time synchronization; SVM: support vector machine; DST: Dempster-Shafer theory of evidence; TA: trust authority; PCA: puzzle-based co-authentication; VLC: visible light communication; NE: Nash equilibrium; RTB: request-to-broadcast; CTB: clear-to-broadcast; RREQ: route request message; RREP: route reply; DDR: data disseminate ratio; Dir: direct trust; IDir: indirect trust; TCE: trust computation error; PDR: packet delivery ratio

A Survey of Trust Management Schemes for Social Internet of Things

Social Internet of Things (SIoT) involves integrating social networking concepts in the Internet of Things (IoT) to enhance social interactions among IoT objects and users. SIoT is envisaged to provide adequate service selection and discovery. Trust is an essential factor whenever social concepts are discussed in communication networks. Trust usually leads to a mutual relationship between two parties (i.e., the trustor and trustee) where they both enjoy mutual benefits. For secure social relationships, Trust management (TM) is a crucial feature of SIoT. The primary aim of this work is to provide a comprehensive review of trust management proposals/schemes available for SIoT. Four main trust calculation algorithms for trust management were selected for this review, and they were examined in detail. The IEEE Xplore, Scopus, ResearchGate, and Google Scholar databases were searched for articles containing the terms "Trust aggregation approaches in IoT", and "Trust computation in SIoT" with a particular emphasis on works published between 2018 and 2021. The paper also discussed the pros and cons of each TM technique, trust metrics/features, contributions, and limitations of the state-of-the-art SIoT TM proposals in the literature. The paper further provides open issues and possible research directions for entry-level researchers in the domain of SIoT.

Vector space modelling-based intelligent binary image encryption for secure communication

In this research work, a secured routing technique is developed based on the LEACH algorithm. Many secure routing algorithms were proposed in the past by modifying the LEACH algorithm with trust management techniques, key generation, and distribution technique and intrusion detection algorithms. Most present solutions are unable to address the issue of modifying and intercepting assaults. To tackle this problem, a new secured routing algorithm is developed in this work that divides the image and messages into sub-components, represent them in a matrix format and add them to get a steganographic image which is encrypted using a suitable encryption algorithm selected by the rule system developed in this research work. Moreover, intelligent agents are incorporated in all the sensor nodes in the network to communicate the encrypted images to the neighbor nodes more reliably.

A Survey of Trust Management Schemes for Social Internet of Things

Social Internet of Things (SIoT) involves integrating social networking concepts in the Internet of Things (IoT) to enhance social interactions among IoT objects and users. SIoT is envisaged to provide adequate service selection and discovery. Trust is an essential factor whenever social concepts are discussed in communication networks. Trust usually leads to a mutual relationship between two parties (i.e., the trustor and trustee) where they both enjoy mutual benefits. For secure social relationships, Trust management (TM) is a crucial feature of SIoT. The primary aim of this work is to provide a comprehensive review of trust management proposals/schemes available for SIoT. Four main trust calculation algorithms for trust management were selected for this review, and they were examined in detail. The IEEE Xplore, Scopus, ResearchGate, and Google Scholar databases were searched for articles containing the terms "Trust aggregation approaches in IoT", and "Trust computation in SIoT" with a particular emphasis on works published between 2018 and 2021. The paper also discussed the pros and cons of each TM technique, trust metrics/features, contributions, and limitations of the state-of-the-art SIoT TM proposals in the literature. The paper further provides open issues and possible research directions for entry-level researchers in the domain of SIoT.

A Trust Management Model for IoT Devices and Services Based on the Multi-Criteria Decision-Making Approach and Deep Long Short-Term Memory Technique.

Recently, Internet of Things (IoT) technology has emerged in many aspects of life, such as transportation, healthcare, and even education. IoT technology incorporates several tasks to achieve the goals for which it was developed through smart services. These services are intelligent activities that allow devices to interact with the physical world to provide suitable services to users anytime and anywhere. However, the remarkable advancement of this technology has increased the number and the mechanisms of attacks. Attackers often take advantage of the IoTs’ heterogeneity to cause trust problems and manipulate the behavior to delude devices’ reliability and the service provided through it. Consequently, trust is one of the security challenges that threatens IoT smart services. Trust management techniques have been widely used to identify untrusted behavior and isolate untrusted objects over the past few years. However, these techniques still have many limitations like ineffectiveness when dealing with a large amount of data and continuously changing behaviors. Therefore, this paper proposes a model for trust management in IoT devices and services based on the simple multi-attribute rating technique (SMART) and long short-term memory (LSTM) algorithm. The SMART is used for calculating the trust value, while LSTM is used for identifying changes in the behavior based on the trust threshold. The effectiveness of the proposed model is evaluated using accuracy, loss rate, precision, recall, and F-measure on different data samples with different sizes. Comparisons with existing deep learning and machine learning models show superior performance with a different number of iterations. With 100 iterations, the proposed model achieved 99.87% and 99.76% of accuracy and F-measure, respectively.

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.

ConTrust: A Novel Context-Dependent Trust Management Model in Social Internet of Things

The global population is around 7.4 billion people. This population density requires connectivity to improve the standard of living by transmitting and receiving variety of services. As a result, numerous forms of communication among objects are required for our everyday living demands, independent of their nature. Furthermore, to create a good relationship, every object that is regarded as associate of another object should have distinct criteria such as scalability, interoperability, and trustworthiness. Many security threats, however, have an impact on the social interaction between objects in a social internet of things (SIoT) context, including illegal admittance and suspicious behavior owing to a lack of verification architecture. Others include attempting to provide a proper viewpoint of a malicious object to earn the trust of other objects. As a result, there is a requirement for an acceptable method to check the behavior of objects such as capability, commitment, reliability, and previous job satisfaction before proceeding with any type of job assignment. This will aid in distinguishing between malicious and trustworthy objects by anticipating their upcoming behavior, allowing better judgments regarding service assignment to be made. This study proposes a context-dependent trust management technique (ConTrust) for choosing and allocating jobs in a SIoT environment. The feature-property match approach, as well as the combination of capability, commitment, and satisfaction, were utilized to increase the efficiency of trust assessment and the resolution of context-dependent difficulties. The proposed trust model considers job characteristics, object capabilities and honesty, and the impact of malicious conduct. The experimental results show that the proposed ConTrust model is viable and capable of ensuring the reliability and efficacy of SIoT service sharing between objects as compared to the benchmark models considered in this work.

ENERGY EFFICIENT BASED SECURE DATA TRANSMISSION FOR MULTI HOP TRUST MANAGEMENT TECHNIQUE USING WIRELESS SENSOR NETWORK

Secure data transfer is intended to keep data safe from illegal access, damage, or disruption. In this proposed technique, an intrusion prevention system is built to counter the rapidly growing threats offered by the current generation of malware, software, and exploits. As the number of intruders has grown, the network environment has become more complicated, making threat mitigation more difficult. Modern wireless sensor networks have emerged for the aim of transmitting important information and services to an ever-growing set of users. Security is the most important issues in wireless network. Using this proposed Multi Hop Trust Management (MHTM) approach; trust management technique is used to identify the trusted nodes with malicious node. Then secured and efficient way of data transmission are directed and communicated to any kind of networks re confirmed here. The result attained shows MHTM technique attains better performance than TSRP in stipulations of energy efficiency, data transmission delay, communication overhead, throughput, malicious sensor device misclassification rate and identification.

Dynamic distributed trust management scheme for the Internet of Things

The Internet of Things (IoT) comprises of a diverse network of homogeneous and heterogeneous nodesthat can be accessed through network ubiquitously. In unattended environments, the IoT devices are prone to variousattacks including ballot-stu?ing, bad-mouthing, self-promotion, on-off, opportunistic behavior attacks, etc. The on-offattack is di?icult to detect as nodes switch their behavior from normal to malicious alternatively. A trust managementmodel is a tool to defend the IoT system against malicious activities and provide reliable data exchange. The majorityof existing IoT trust management techniques are based on static reward and punishment values in pursuit of trustcomputation thereby allowing the misbehaving nodes to deliberately perform on-off attacks. Due to the static nature ofawarding scores, these schemes fail to identify malicious nodes in certain cases. In this paper, a dynamic and distributedtrust management scheme (DDTMS) is proposed where nodes can autonomously evaluate peer nodes' behavior anddynamically grant reward and penalty score. The proposed scheme successfully detects the on-off attack and isolates themisbehaving nodes thereby classifying them into three distinct categories based on their severity levels i.e. low, mild,and severe. Simulation-based performance evaluation shows improved performance of the proposed DDTMS againstother state-of-the-art schemes thereby requiring less time and fewer interactions for successfully identifying malevolentbehavior of compromised nodes.

Secure cluster head election algorithm and misbehavior detection approach based on trust management technique for clustered wireless sensor networks

Trust management is an effective technique for dealing with malicious and compromised nodes in Wireless Sensor Networks (WSNs). It has demonstrated its benefits in various issues such as secure cluster head (CH) election, secure localization, secure routing and misbehavior detection. This work presents a secure CH election algorithm and a misbehavior detection approach. Multiple metrics were used for the CH election, including the key metric for the election which is the trust degree of the sensor node. The problem of selecting the most trustworthy node as CH was also addressed. In addition, a monitoring strategy to evaluate the behavior of sensor nodes using multiple trust types was developed. Thus, the aim was to keep only the trustworthy nodes in the network and eliminate the malicious nodes. The case of a compromised CH was considered; a trust evaluation mechanism at the cluster members level and a local clustering algorithm were adopted to isolate the malicious CH without affecting the network performance. The simulation results indicate that the proposed scheme prevents the malicious nodes from becoming CHs and protects the network from compromised CH after the election. With respect to the misbehavior detection, the proposed scheme achieved a high detection rate of malicious nodes with a low number of false positive and false negative alarms.

RETRACTED ARTICLE: Intelligent fuzzy rule-based approach with outlier detection for secured routing in WSN

In wireless sensor networks (WSNs), energy optimization and the provision of security are the major design challenges. Since the wireless sensor devices are energy constrained, the issue of high energy consumption by the malicious nodes must be addressed well in order to enhance the network performance by making increased network lifetime, reduced energy consumption and delay. In the past, many researchers worked in the provision of new techniques for providing improved security to WSN in order to enhance the reliability in the routing process. However, most of the existing routing techniques are not able to achieve the required security through the use of intelligent techniques for safeguarding the sensor nodes from malicious attacks. In order to address these problems, a new fuzzy temporal clustering-based secured communication model with trust analysis and outlier detection has been developed in this research work. For this purpose, a new fuzzy temporal rule-based cluster-based routing algorithm with trust modelling and outlier detection for monitoring the nodes participating in the communication has been proposed. In addition, a fuzzy temporal rule- and distance-based outlier detection algorithm is also proposed in this paper for distinguishing the malicious nodes from other nodes within each cluster of the network and has been used in the secured routing algorithm. The proposed secure routing algorithm uses the temporal reasoning tasks of explanation-based learning and prediction as well as spatial constraints for making efficient routing decisions through the application of trust and key management techniques for performing effective authentication of nodes and thereby isolating the malicious nodes from communication through outlier detection. By applying these two proposed algorithms for communication in the proposed work, it is proved through experiments that the proposed secure routing algorithm and the outlier detection algorithm are able to perform secured and reliable routing through genuine cluster head nodes more effectively. Moreover, these two algorithms provide improved quality of service with respect to the reliability of communication, packet delivery ratio, reduction in end-to-end delay and reduced energy consumption.

HTMS: Fuzzy Based Hierarchical Trust Management Scheme in WSN

The state of the art trust management techniques especially designed for wireless sensor network, are not well suited due to less battery power and less memory of sensor nodes. In this work, we propose a fuzzy based hierarchical trust management scheme which includes direct trust calculation on real time past experience and credit based calculation and indirect trust calculation on peer recommendation. In this scheme cluster head and base station maintains constructive knowledge table based on fuzzy logic which reduces memory and communication overhead. As a whole the scheme reduces the communication overhead, computational time and memory utilization because it deals with decision rather than data compare to other existing schemes.

A Comprehensive Study on the Trust Management Techniques in the Internet of Things

Internet of Things (IoT) has been developed as one of the most significant technology in the future of the Internet, in which the physical objects are transformed into smart objects that can be handled and monitored via the Internet. The trust management takes a significant role in the IoT for enabling trustworthy data collection, context-awareness, and enhanced user privacy. Despite the critical significance of trust management techniques in the IoT, there is not any organized and comprehensive study in this field. Therefore, the aim of this article is to review the available methods in this field in a systematic way. In this regard, the selected techniques are categorized into four main classes, including recommendation-based, prediction-based, policy-based, and reputation-based. Then they are discussed and also compared based on some trust metrics, such as accuracy, adaptability, availability, heterogeneity, integrity, privacy, reliability, and scalability. Furthermore, some hints and challenges for further studies are outlined.