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The Internet of Things (IoT) connects objects that sense, communicate, and interact to achieve shared goals, and its integration with manufacturing has led to Industry 4.0. Security is crucial for user acceptance, involving data confidentiality, integrity, authentication, identity management, privacy, and trust among devices. Trust management is vital in IoT and IIoT for reliable services, accurate data collection, device authentication, and secure decision-making. This paper surveys blockchain-based trust mechanisms in IoT and IIoT, categorizing them into four types: basic trust frameworks, enhanced trust interaction, trust data management, and trust-based transaction validation. We analyze these mechanisms to identify their strengths, weaknesses, and current challenges. Blockchain shows promise in improving trust management by decentralizing trust, ensuring transparency, and enhancing security. However, challenges like scalability, energy consumption, and integration with existing IoT infrastructure remain. This survey aims to guide future research by highlighting these issues and proposing potential solutions, contributing to the development of blockchain-based trust management systems in IoT and IIoT. [ABSTRACT FROM AUTHOR]

The emerging and promising paradigm of the Internet of Vehicles (IoV) employ vehicle-to-everything communication for facilitating vehicles to not only communicate with one another but also with the supporting roadside infrastructure, vulnerable pedestrians, and the backbone network in a bid to primarily address a number of safety-critical vehicular applications. Nevertheless, owing to the inherent characteristics of IoV networks, in particular, of being (a) highly dynamic in nature and which results in a continual change in the network topology and (b) non-deterministic owing to the intricate nature of its entities and their interrelationships, they are susceptible to a number of malicious attacks. Such kinds of attacks, if and when materialized, jeopardizes the entire IoV network, thereby putting human lives at risk. Whilst the cryptographic-based mechanisms are capable of mitigating the external attacks, the internal attacks are extremely hard to tackle. Trust, therefore, is an indispensable tool since it facilitates in the timely identification and eradication of malicious entities responsible for launching internal attacks in an IoV network. To date, there is no dataset pertinent to trust management in the context of IoV networks and the same has proven to be a bottleneck for conducting an in-depth research in this domain. The manuscript-at-hand, accordingly, presents a first of its kind trust-based IoV dataset encompassing 96,707 interactions amongst 79 vehicles at different time instances. The dataset involves nine salient trust parameters, i.e., packet delivery ratio, similarity, external similarity, internal similarity, familiarity, external familiarity, internal familiarity, reward/punishment, and context, which play a considerable role in ascertaining the trust of a vehicle within an IoV network. Dataset:  https://github.com/wangyingxun/IoV. Dataset License: Creative Commons Attribution 4.0 International. [ABSTRACT FROM AUTHOR]

Vehicular Ad-Hoc Networks (VANETs) are pivotal to the advancement of intelligent transportation systems (ITS), enhancing safety and efficiency on the road through secure communication networks. However, the integrity of these systems is severely threatened by Distributed Denial-of-Service (DDoS) attacks, which can disrupt the transmission of safety-critical messages and put lives at risk. This research paper focuses on developing robust detection methods and countermeasures to mitigate the impact of DDoS attacks in VANETs. Utilizing a combination of statistical analysis and machine learning techniques (i.e., Autoencoder with Long Short-Term Memory (LSTM), and Clustering with Classification), the study introduces innovative approaches for real-time anomaly detection and system resilience enhancement. Emulation results confirm the effectiveness of the proposed methods in identifying and countering DDoS threats, significantly improving (i.e., 94 percent anomaly detection rate) the security posture of a high mobility-aware ad hoc network. This research not only contributes to the ongoing efforts to secure VANETs against DDoS attacks but also lays the groundwork for more resilient intelligent transportation systems architectures. [ABSTRACT FROM AUTHOR]

Purpose: The aim of the research conducted was to try to identify the competences of managers that positively influence employee trust. The relationship identified between managers' competences and employees' trust seems extremely interesting. It became the inspiration for the considerations presented in this article. Design/methodology/approach: The survey was conducted using the CAWI method, based on the author's survey questionnaire with a total of 14 questions. A Likert scale was used in the research process. Findings: The collected factual material exposes the special role in the process of building intra-organisational trust of such groups of competences as communication or human capital management. In addition, the studied influence of personality traits and trust determinants made it apparent that, for employees, one of the most important characteristics of a manager is emotional maturity. Research limitations/implications: The analysis of the survey results was carried out on the basis of 216 correctly completed questionnaires. Further research should be carried out on a larger number of respondents, taking into account industries and professional groups. Originality/value: This article is based on a thorough analysis of the literature on the topic and the authors' own research. It presents a model of managers' competences positively influencing employees' trust, developed on the basis of factual material. It is addressed to researchers dealing with the subject and managers seeking solutions in building intra-organisational trust. [ABSTRACT FROM AUTHOR]

Compliance with security requirements in the fog computing environment is known as an important phenomenon in maintaining the quality of service due to the dynamic topology. Security and privacy breaches can occur in fog computing because of its properties and the adaptability of its deployment method. These characteristics render current systems inappropriate for fog computing, including support for high mobility, a dynamic environment, geographic distribution, awareness of location, closeness to end users, and absence of redundancy. Although efficient secure routing protocols have been developed by researchers in recent years, it is challenging to ensure security, reliability, and quality of service at the same time to overcome the limitations of cloud-fog computing. In light of the fact that trust management is an effective means of protecting sensitive information, this study proposes a two-way trust management system (TMS) that would enable both the service requester and the service provider to verify each other's reliability and safety. The trustworthiness of the service seeker can also be verified in this way. So that fog clients can confirm that fog nodes can deliver suitable, dependable, and secure services, trust in a fog computing environment should ideally be two-way. The ability to verify the authenticity of fog clients is an important capability for fog nodes to have. A distributed, event-based, multi-trust trust system is presented by the suggested approach to trust computation, which makes use of social relationships (nodes and clients) and service quality criteria. Hence, the trust score is computed using a number of characteristics. Here, the weight of direct and indirect ratings is emphasized, and the final trust score is computed by dynamically merging the information gained from self-observation and the suggestions of nearby nodes. An extensive evaluation of the proposed method shows that it is resistant to a large number of badly behaved nodes and can successfully neutralize trust-based attacks. [ABSTRACT FROM AUTHOR]

The purpose of the paper is to present the issues relating to methods of transport and commercial process management and support in logistics. Moreover, it will describe shipping actions in relation to the conflicts in Europe and worldwide. The study follows the work plan described as follows: The first chapter comprises an overview, logistics function, and classification of strategies in a logistics project. The second chapter includes information about the review and selection of methods of logistics project management. The third chapter pertains to the application of solutions restoring an old form of value stream mapping, known as the brown book exercise or the brown paper method. The fourth chapter refers to the aspects and solutions used in logistics project management in the hard times of logistic service market operations. Finally, the fifth chapter specifies the forms of trust management as the methods supporting logistics project management.

In a permissionless social network environment, it is difficult for a user to assess the trustworthiness of other users. Thus, trust management becomes a crucial issue for securing social activities. The conventional method for assessing trust involves an authority to identify fakes. However, generative AI is gradually reducing the ability to distinguish by producing vivid news or images. Additionally, modern social networks are becoming flatter and more diverse, making conventional trust management methods unfeasible. There is an urgent need to design a suitable blockchain consensus for decentralized trust management. In this paper, we propose a novel three-layer autonomous trust management model for online social users based on blockchain technology and group decision making. Instead of solving a hash puzzle, which consumes too much computational resources for social network users, this paper proposes a lightweight and verifiable blockchain consensus proof of trust for decentralized trust management. Furthermore, performance analysis and simulation results show that our model is valid and flexible for both synthetic networks and real social networks. [ABSTRACT FROM AUTHOR]

The growing importance of edge and fog computing in the modern IT infrastructure is driven by the rise of decentralized applications. However, resource allocation within these frameworks is challenging due to varying device capabilities and dynamic network conditions. Conventional approaches often result in poor resource use and slowed advancements. This study presents a novel strategy for enhancing resource allocation in edge and fog computing by integrating machine learning with the blockchain for reliable trust management. Our proposed framework, called CyberGuard, leverages the blockchain's inherent immutability and decentralization to establish a trustworthy and transparent network for monitoring and verifying edge and fog computing transactions. CyberGuard combines the Trust2Vec model with conventional machine-learning models like SVM, KNN, and random forests, creating a robust mechanism for assessing trust and security risks. Through detailed optimization and case studies, CyberGuard demonstrates significant improvements in resource allocation efficiency and overall system performance in real-world scenarios. Our results highlight CyberGuard's effectiveness, evidenced by a remarkable accuracy, precision, recall, and F1-score of 98.18%, showcasing the transformative potential of our comprehensive approach in edge and fog computing environments. [ABSTRACT FROM AUTHOR]

In socially aware networking, encryption algorithms are used in privacy-preserving algorithms to provide security for the nodes. However, the shared key lacks certain security management during transmission, which can affect the security of messages delivery. Therefore, this paper proposes a Self-organizing Key Security Management Algorithm (SKSM) to manage the secure transmission of keys used in encryption algorithms in socially aware networking. The key management algorithm comprises three main parts: node authentication protocol, node trust management, and key slice transmission. Firstly, zero-knowledge authentication and trust management are utilized to authenticate and manage the network nodes. Then, threshold cryptographic segmentation of keys is performed for transmission. After the key reaches the threshold, the key is recovered to ensure that the encrypted information will not be leaked or intercepted during transmission. The simulation experimental results demonstrate that the SKSM algorithm, compared with the traditional algorithms more effective than the traditional algorithm in ensuring secure message transmission, improving transmission rates, and reducing average network transmission latency. [ABSTRACT FROM AUTHOR]

Zero-trust security is a security model that assumes no entity is implicitly trusted, regardless of its origin or scope of access. This approach requires continuous verification of all users, devices, and applications before granting access to resources. Cloud computing is a model for delivering IT resources and applications as a service over the Internet. Cloud computing offers many benefits, including scalability, agility, and cost savings. However, cloud computing also introduces new security challenges. This paper proposes a survey-based research methodology to evaluate the usage of clouds in zero-trust security strategies. The paper identifies different zero-trust security solutions, their key features, and the benefits and challenges of implementing these solutions in the cloud. The paper will also discuss the costs and benefits of different zero-trust security solutions. The findings of this research will be valuable for organizations that are considering implementing a zero-trust security strategy in the cloud. The paper will provide guidance on how to choose the best zero-trust security solution for organizational needs and how to implement it effectively. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are crucial in various fields including Health Care Monitoring, Battlefield Surveillance, and Smart Agriculture. However, WSNs are susceptible to malicious attacks due to the massive quantity of sensors within them. Hence, there is a demand for a trust evaluation framework within WSNs to function as a secure system, to identify and isolate malicious or faulty sensor nodes. This information can be leveraged by neighboring nodes, to prevent collaboration in tasks like data aggregation and forwarding. While numerous trust frameworks have been suggested in the literature to assess trust scores and examine the reliability of sensors through direct and indirect communications, implementing these trust evaluation criteria is challenging due to the intricate nature of the trust evaluation process and the limited availability of datasets. This research conducts a novel comparative analysis of three trust management models: "Lightweight Trust Management based on Bayesian and Entropy (LTMBE)", "Beta-based Trust and Reputation Evaluation System (BTRES)", and "Lightweight and Dependable Trust System (LDTS)". To assess the practicality of these trust management models, we compare and examine their performance in multiple scenarios. Additionally, we assess and compare how well the trust management approaches perform in response to two significant cyber-attacks. Based on the experimental comparative analysis, it can be inferred that the LTMBE model is optimal for WSN applications emphasizing high energy efficiency, while the BTRES model is most suitable for WSN applications prioritizing critical security measures. The conducted empirical comparative analysis can act as a benchmark for upcoming research on trust evaluation frameworks for WSNs. [ABSTRACT FROM AUTHOR]

Trust management has been shown to be an effective technique for protecting networks from malicious nodes and ensuring wireless sensor network (WSN) security. A number of trust systems have been proposed, but most of them are not adaptative to the current state of network security and the intensity of the attacks to which they are subjected, especially in the case of collaborative attacks. They employ fixed trust metrics derived from expert opinion rather than the objective method based on the network's current security level. Furthermore, they are complex trust systems designed for a specific application with a high attack probability. Thus, even with a low attack rate, they consume a lot of energy. This paper proposes an adaptive trust system that considers both the network's risk level and the trust values of sensor nodes at the same time. To match the situation in the network, the proposed system employs various trust policies. In risky situations where the WSN environment remains untrustworthy, the proposed system adjusts its trust metrics based on the network attack intensity. When the attacks are eliminated and the misbehavior rate is low, the system switches to an energy efficient policy and adjusts its trust metrics to conserve sensor node energy. Simulation results show that a zero-tolerance policy achieves 95% of the detection rate and conserves 50% of nodes' energy under the presence of 35% of malicious nodes in the network. Energy efficient policy achieves 90% of detection rate and conserves 95% of nodes' energy under the existence of 10% of malicious nodes in the network. Normal policy achieves up to 90% of detection rate between 15 and 25% of malicious nodes while conserving 70% and 80% of energy under these percentages. [ABSTRACT FROM AUTHOR]

The Social Internet of Vehicles (SIoV) is an emerging network that interconnects vehicles and generates vast amounts of data concerning social relationships, chauffeurs, commuters, as well as surrounding conditions. However, security is a major concern in SIoV networks, especially during data transmission. In order to solve this problem, we propose a novel approach called SIoV-FTFSA-CAOA, which stands for Social Internet of Vehicles—Fuzzy-based Trustworthy Friendship Selection Algorithm—Crossover-boosted Arithmetic Optimization Algorithm, to enhance security in SIoV networks. The proposed method uses fuzzy logic to map input factors to an output trust score, indicating the level of trust between vehicles. The Crossover-boosted Arithmetic Optimization Algorithm is used to identify communities based on modularity measure values that indicate the quality of community structures. The highest trust value selection algorithm is then used to select trustworthy friends in each community based on high trust scores, ensuring secure communication. The Road Aware Geographical Routing protocol is utilized to establish the routing path between vehicles, and the Battery Model-based Energy Computation method is used to estimate the consumption of energy to the established path. The proposed algorithm selects the most energy-efficient routing path to share information securely among vehicles. The finding exhibits that the proposed method achieves large packet delivery ratios, security, accuracy, and minimal energy consumption compared to existing methods. Furthermore, the proposed method performs better against various attacks, including jamming attacks, impersonation attacks, false data injection attacks, and bad-mouthing attacks. Hence, the proposed method is suitable for secure data transmission in the SIoV networks. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are increasingly being used in a variety of applications, such as smart healthcare systems and the Internet of Things (IoT). Trust in WSNs is the confidence in secure, efficient collaboration among nodes, crucial for reliable data packet routing. However, their susceptibility to trust-related challenges such as different threats, high energy use, and communication issues among nodes, pose substantial challenges to data integrity and availability. Recognizing the pivotal role of trust in ensuring secure data exchange and dependable device communication within the IoT framework, this paper introduces a novel trust-aware optimization technique. The approach integrates trust management into routing protocols, employing the resilient decision-making algorithm Dempster-Shafer Theory (DST) for trusted clustering and the Whale Optimization Algorithm (WOA) for routing. The primary objective is to enhance the reliability and energy efficiency of WSNs by incorporating trustworthiness considerations into optimal cluster and route selection. The proposed DST-WOA technique is evaluated using the MATLAB Simulator, demonstrating a significant 23%, 22%, and 4% improvement over existing protocols (TBSIOP, SQEER, SSDBN-TAA) in network lifetime, energy efficiency and throughput, respectively. The proposed research contributes to the development of more robust and sustainable WSNs for applications in areas such as smart healthcare systems, and IoT. [ABSTRACT FROM AUTHOR]

With the rapid development of intelligent transportation, vehicular ad hoc networks (VANETs) has broad development prospects, but also faces a variety of security threats. A distributed hybrid trust management scheme called HTMS-V is proposed for insider attackers and false message detection in VANETs. Specifically, considering the characteristics of VANETs, node trust is evaluated based on the improved subjective logic model. The evaluation combines direct trust and indirect trust, and the trust relationship between nodes is established based on interaction records. Message trust is evaluated based on node trust and the distance between nodes. And the scheme identifies false messages and malicious nodes based on the trust evaluation results. This paper tests the performance of the HTMS-V scheme under four attack scenarios. The simulation results show that the HTMS-V scheme effectively resists various attacks in VANETs and is able to identify most false messages and malicious nodes even if the malicious node ratio reaches 40%, and the performance of the HTMS-V scheme is obviously better than that of the baseline scheme which is composed of subjective logical model and distance based weighted voting.

Mobile ad hoc networks (MANETs) are wireless multi-hop networks that do not rely on any fixed infrastructure, unlike traditional networks. Nodes in MANETs are formed dynamically and are free to move in any direction at variable speeds. The special characteristics of MANETs make them vulnerable to flooding attacks, which can have a negative impact on their performance. Moreover, due to their nature, employing solutions designed for traditional networks is not feasible. One potential solution to enhance the performance of MANETs in the face of network attacks is to implement trust management. This paper evaluates the performance of Ad hoc On-Demand Distance Vector (AODV) Routing in the presence of a flooding attack. We propose a direct trust management scheme to detect and isolate malicious nodes and implement this scheme on AODV. We name the modified protocol Trusted AODV (TAODV) and, finally, compare the performance of AODV and TAODV when both are under a flooding attack to measure the improvement achieved by our suggested scheme. [ABSTRACT FROM AUTHOR]

The purpose of the paper is to present the issues relating to methods of transport and commercial process management and support in logistics. Moreover, it will describe shipping actions in relation to the conflicts in Europe and worldwide. The study follows the work plan described as follows: The first chapter comprises an overview, logistics function, and classification of strategies in a logistics project. The second chapter includes information about the review and selection of methods of logistics project management. The third chapter pertains to the application of solutions restoring an old form of value stream mapping, known as the brown book exercise or the brown paper method. The fourth chapter refers to the aspects and solutions used in logistics project management in the hard times of logistic service market operations. Finally, the fifth chapter specifies the forms of trust management as the methods supporting logistics project management. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of the L.N. Gumilyov Eurasian National University. Law Series is the property of L.N. Gumilyov Eurasian National University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rapid development of communication technologies, the demand for security and automation of driving has promoted the development of the Internet of Vehicles (IoV). The IoV aims to provide users with a safer, more comfortable, and more efficient driving experience. However, the current IoV also faces a series of potential security risks and privacy breaches, which has further propelled research on trust management for vehicular networks. The introduction of the blockchain has resolved the issue of data security in IoV trust management. However, the blockchain is limited by its own performance and scalability, making it unsuitable for large-scale networks. In order to enhance the transaction-processing efficiency of blockchain-based trust management solutions and address their scalability limitations, this paper presents a graph partition-based blockchain-sharding protocol. Simulation results on real-world datasets demonstrate that the proposed scheme exhibits better scalability compared to existing blockchain-based approaches and can accommodate larger-scale device access. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) bring both potential and difficulties for emergency applications, including packet loss and changes in network topology. UAVs are also quickly taking up a sizable portion of the airspace, allowing Flying Adhoc NETworks (FANETs) to conduct effective ad hoc missions. Therefore, building routing protocols for FANETs is difficult due to flight restrictions and changing topology. To solve these problems, a bio-inspired route selection technique is proposed for FANET. A combined trustworthy and bioinspired-based transmission strategy is developed as a result of the growing need for dynamic and adaptable communications in FANETs. The fitness theory is used to assess direct trust and evaluate credibility and activity to estimate indirect trust. In particular, assessing UAV behavior is still a crucial problem in this field. It recommends fuzzy logic, one of the most widely utilized techniques for trusted route computing, for this purpose. Fuzzy logic can manage complicated settings by classifying nodes based on various criteria. This method combines geocaching and unicasting, anticipating the location of intermediate UAVs using 3-D estimates. This method guarantees resilience, dependability, and an extended path lifetime, improving FANET performance noticeably. Two primary features of FANETs that shorten the route lifetime must be accommodated in routing. First, the collaborative nature necessitates communication and coordination between the flying nodes, which uses a lot of energy. Second, the flying nodes' highly dynamic mobility pattern in 3D space may cause link disconnection because of their potential dispersion. Using ant colony optimization, it employs trusted leader drone selection within the cluster and safe routing among leaders. a fuzzy-based UAV behavior analytics is presented for trust management in FANETs. Compared to existing protocols, the simulated results demonstrate improvements in delay routing overhead in FANET. [ABSTRACT FROM AUTHOR]

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Based on trust management, the service establishment between IoT/User devices and fog networks reduces the impact of malicious data and nodes. Hence, building a trusted environment in the IoT and fog layers is very important. There is no assurance that the generated data or task is standard data; it may also have abnormal data to modify its originality. Similarly, the nodes in the fog environment also have malicious nodes during task offloading. For this, the novel Two-way IoT data & Fog Trust management (TwI-FTM) algorithm is proposed to identify the abnormal data with the help of the Trusted Data Measurement (TDM) and Fog Access Trust Evaluation (FATE) method for identifying the malicious fog access nodes in fog network. Identifying these abnormal data and malicious fog access nodes is done by continuously monitoring fog control nodes with the threshold level. Once it is identified, they will automatically be removed from the IoT-FoG network. The obtained trust measurements are analyzed and compared with other existing methods; it is identified that the proposed TwI-FTM outperforms and has a higher trust degree of 0.68 compared with others. It achieved higher detection accuracy of malicious fog access nodes in fog networks at 21 %, 16 %, and 14 % compared with OTM, TTM, and SLA-Trust, respectively. Finally, the task completion time was also reduced, and it was achieved at 4.5 ms, 5 ms, and 13 ms compared with TTM, OTM, and SLA-Trust, respectively.

The Internet of Things (IoT) plays a crucial role in our daily lives. Many objects are being equipped with electronic components in order to facilitate their interconnection and integration with the Internet. Due to the growing usage of IoT devices and generated data, to ensure the reliability of the network, it is necessary to utilize scalable and effective trust management systems. This study introduces the SCoTMan model, which integrates social interactions with the capability of smart contracts for effective trust management in Social IoT (SIoT). Blockchain can enhance trust management protocols by providing a global view of data and improving the propagation of trust values. Implementing trust management algorithms in traditional decentralized architectures can be challenging, but using smart contracts can provide an efficient solution. We implemented the model on the Hyperledger Fabric platform and performed a comprehensive assessment. The model efficiently performs indirect trust computations by choosing optimized counselors and minimizing storage requirements. Multiple experiments have been carried out to assess and validate the efficiency of the model in functionality metrics such as trust convergence and success rate, as well as non-functional metrics like transaction delay, computation and communication overhead, and memory usage. We also conducted a comparative analysis to emphasize the method’s advantages over existing approaches in the literature. We introduced the Total Storage Cost metric for better comparison. The results show substantial improvements in trust convergence under limited Total Storage Cost. The proposed method demonstrates that it is possible to establish scalable trust management in the SIoT by utilizing smart contracts and social metrics.

The global surge in population, coupled with the continuous emergence of new digital devices, has significantly increased the worldwide demand for electricity, intensifying the existing energy crisis. Microgrids are pivotal in addressing the current needs of utility industries and driving innovation. However, numerous challenges remain, particularly in developing countries where inadequate infrastructure hampers electricity generation, transmission, and distribution. A potential solution lies in energy sharing, which offers mutual benefits to both microgrid operators and consumers. The integration of Internet of Things (IoT) technology within microgrids enhances real-time monitoring and management of energy resources, thereby improving both efficiency and reliability. Microgrids have the capability to share electricity resources with other microgrids, local communities, or contribute surplus energy to the national grid. However, maintaining the integrity of participants in these energy exchanges is crucial. It is necessary to distinguish between trustworthy and potentially malicious microgrids or consumers. To address this issue, the implementation of a MicroTrust mechanism is proposed. The results demonstrate that MicroTrust effectively evaluates the trustworthiness of CCs and various requesting consumer nodes, providing a robust solution for secure and reliable energy sharing.

As next-generation networking environments become increasingly complex and integral to the fabric of digital transformation as the traditional perimeter-based security model proves inadequate. The Zero Trust framework emerges as a critical solution to this challenge, advocating for a security model that assumes no implicit trust and requires verification at every step. The rapid growth of the Internet of Things (IoT) creates an environment of millions of interacting devices that radically transform today’s digital environment. In such conditions, the problem of identifying malicious and compromised nodes among IoT devices becomes mandatory to maintain trustworthy environment. The main objective of this research is to implement an advanced trust management mechanism, with the main transformation on the framework of security within which IoT environments become workable. To overcome this, an approach based on intelligent ensemble learning is presented in this paper by formulating a dataset consists of IoT-23, N-BaIoT, Edge-IIoTset, and AutoTrust-IoTDS. This work is specialized by selecting a diversified combination of features from these datasets to construct an effective dataset that would further enhance inculcation of trust in the IoT networks. The study proposed two notable models, i.e., XGBoost with logistic regression and AdaBoost with decision tree to achieve the efficiency and scalability of the proposed models. The results shows significant improvement in the efficiency, and having both the mentioned models to offer a precision, recall and F1-score of 0.99 representing efficient mechanism in ensuring a secure and dependable IoT ecosystem.

Healthcare management is a major application of future Internet of Things (IoT)-based smart cities. The healthcare applications rely on the use of Internet of Things (IoT) devices, which consist of sensor networks with diverse communication infrastructure. This makes it more vulnerable and increases the malicious attacks resulting in compromised Quality of Service (QoS) and anomalies in data transmission resulting in transmission delay of the legitimate nodes in the network. This may cause life threats in the healthcare system as critically ill patients’ data needs to be delivered to the centralized healthcare centre of the smart cities. In this work, a Trust-based Improved QoS for Health Care System (TIHCS) is proposed. TIHCS offers a Time Division Multiple Access (TDMA) based slots allocation mechanism for legitimate nodes by introducing a malicious detection mechanism. In addition, it proposes a mechanism based on the Intelligent Reflecting Surfaces (IRS) to restrict the communication of the specific area where a malicious node is present and allow legitimate nodes of the affected area through relay node selection by proposing a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The simulation results show that TOPSIS-based relay selection in TIHCS offers a better secrecy sum rate as compared to the other three methods. Furthermore, the proposed trust-supported TDMA-based Media Access Control (MAC) protocol offers improved data transmission of the most sensitive data traffic, by allowing more highly sensitive data nodes to transmit their data as compared to the well-established standards such as First Come First Serve, Round Robin, Shortest Job First, and Longest Job First.

Zero-trust security is a security model that assumes no entity is implicitly trusted, regardless of its origin or scope of access. This approach requires continuous verification of all users, devices, and applications before granting access to resources. Cloud computing is a model for delivering IT resources and applications as a service over the Internet. Cloud computing offers many benefits, including scalability, agility, and cost savings. However, cloud computing also introduces new security challenges. This paper proposes a survey-based research methodology to evaluate the usage of clouds in zero-trust security strategies. The paper identifies different zero-trust security solutions, their key features, and the benefits and challenges of implementing these solutions in the cloud. The paper will also discuss the costs and benefits of different zero-trust security solutions. The findings of this research will be valuable for organizations that are considering implementing a zero-trust security strategy in the cloud. The paper will provide guidance on how to choose the best zero-trust security solution for organizational needs and how to implement it effectively.

A growing kind of communication, the Internet of Things (IoT), links all network-capable devices worldwide. The most recent phase of the Internet of Things (IoT), known as the Internet of Medical Things (IoMT), is one that is rapidly catching researchers’ interest. The centralized storage system houses the substantial amount of medical data created by IoMT. However, centralizing sensitive patient data creates a single point of failure and raises privacy and security issues. To assure honesty, reliability, and safety while determining an entity’s trustworthiness, trust information is required to be safely transmitted and preserved. We propose a safe trust management solution based on blockchain technology. The proposed solution gathers node trust data using both time- and event-driven methods that are used to compute the trust of a node based on the threshold value. We also confirm the validity of data by varying the threshold value during transmission and authentication and employing a membership mechanism for authentication. The trust scores for each node are securely stored in an array, and the Maximum subarray (Kadane) Algorithm is used to compute the threshold value. The blockchain network receives and stores the IoMT score in the trusted list. Due to its distributed structure, capacity to maintain safeguards, and resiliency against a variety of threats with low overhead. Our approach is viable, deployable, and appropriate and also includes safety features like tamper-proofing, attack resistance, dependability, and low capabilities for IoT in smart hospitals.

Supply chains increasingly develop toward complex networks, both technically in terms of devices and connectivity, and also anthropogenic with a growing number of actors. The lack of mutual trust in such networks results in challenges that are exacerbated by stringent requirements for shipping conditions or quality, and where actors may attempt to reduce costs or cover up incidents. In this paper, we develop and comprehensively study four scenarios that eventually lead to end-to-end-secured sensing in complex IoT-based supply chains with many mutually distrusting actors, while highlighting relevant pitfalls and challenges—details that are still missing in related work. Our designs ensure that sensed data is securely transmitted and stored, and can be verified by all parties. To prove practical feasibility, we evaluate the most elaborate design with regard to performance, cost, deployment, and also trust implications on the basis of prevalent (mis)use cases. Our work enables a notion of secure end-to-end sensing with minimal trust across the system stack, even for complex and opaque supply chain networks.

The Denial of Service (DoS) attack threatens the availability of key components of Vehicular Ad-hoc Network (VANET). Various centralized and decentralized trust-based approaches have been proposed to secure the VANET from DoS attack. The centralized approach is less efficient because the attack on the central trust manager leads to the overall failure of services. In comparison, the cluster-based decentralized approach faces overhead because of frequent changes in cluster members due to the high speed of the vehicles. Therefore, we have proposed a cluster-based Denial-of-Service Resistant Trust model (DoSRT). It improves decentralized trust management using speed deviation-based clustering and detects DoS attack based on the frequency of messages sent. Through performance evaluation, we have found that DoSRT improves precision, recall, accuracy, and F-Score by around 19%, 16%, 20%, and 17% in the presence of 30% DoS attackers.

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

Vehicular Ad-Hoc Networks (VANETs) are pivotal to the advancement of intelligent transportation systems (ITS), enhancing safety and efficiency on the road through secure communication networks. However, the integrity of these systems is severely threatened by Distributed Denial-of-Service (DDoS) attacks, which can disrupt the transmission of safety-critical messages and put lives at risk. This research paper focuses on developing robust detection methods and countermeasures to mitigate the impact of DDoS attacks in VANETs. Utilizing a combination of statistical analysis and machine learning techniques (i.e., Autoencoder with Long Short-Term Memory (LSTM), and Clustering with Classification), the study introduces innovative approaches for real-time anomaly detection and system resilience enhancement. Emulation results confirm the effectiveness of the proposed methods in identifying and countering DDoS threats, significantly improving (i.e., 94 percent anomaly detection rate) the security posture of a high mobility-aware ad hoc network. This research not only contributes to the ongoing efforts to secure VANETs against DDoS attacks but also lays the groundwork for more resilient intelligent transportation systems architectures.

The emerging and promising paradigm of the Internet of Vehicles (IoV) employ vehicle-to-everything communication for facilitating vehicles to not only communicate with one another but also with the supporting roadside infrastructure, vulnerable pedestrians, and the backbone network in a bid to primarily address a number of safety-critical vehicular applications. Nevertheless, owing to the inherent characteristics of IoV networks, in particular, of being (a) highly dynamic in nature and which results in a continual change in the network topology and (b) non-deterministic owing to the intricate nature of its entities and their interrelationships, they are susceptible to a number of malicious attacks. Such kinds of attacks, if and when materialized, jeopardizes the entire IoV network, thereby putting human lives at risk. Whilst the cryptographic-based mechanisms are capable of mitigating the external attacks, the internal attacks are extremely hard to tackle. Trust, therefore, is an indispensable tool since it facilitates in the timely identification and eradication of malicious entities responsible for launching internal attacks in an IoV network. To date, there is no dataset pertinent to trust management in the context of IoV networks and the same has proven to be a bottleneck for conducting an in-depth research in this domain. The manuscript-at-hand, accordingly, presents a first of its kind trust-based IoV dataset encompassing 96,707 interactions amongst 79 vehicles at different time instances. The dataset involves nine salient trust parameters, i.e., packet delivery ratio, similarity, external similarity, internal similarity, familiarity, external familiarity, internal familiarity, reward/punishment, and context, which play a considerable role in ascertaining the trust of a vehicle within an IoV network.

IoT device applications are rapidly expanding in the current environment. Today, we can find IoT devices in a variety of industries, including agriculture, home security, entertainment, health, transportation, and education. Today, users of IoT devices and applications come from large cities, small towns, and rural areas. Security and trust are vital areas for the success of IoT implementation in IoT applications. When IoT devices engage with one another, trust grows between them. IoT device communications may be made secure by implementing trust-featured systems. Algorithms based on Machine learning, Fuzzy logic, Bayesian, and Entropy can be utilized for trust management systems. Trust management systems are surveyed in this study. Further, this paper highlights research challenges and directions in this area. [ABSTRACT FROM AUTHOR]

Decentralised fog computing can provide real-time interaction, minimize latency, heterogeneity, and provide networking services between edge devices and cloud data centers. One of the biggest challenges in the fog layer network is finding a trustworthy fog node. Trust management encompasses the process of being trustworthy and the act of assessing the reliability of other nodes. It is essential to carry out a comprehensive review using a systematic approach in this field to advance our understanding, address emerging challenges, and foster secure and efficient trust management practices. This research paper considers a comprehensive analysis of high-quality fog computing trust management literature from 2018 to 2022. A variety of distinct approaches have been chosen by fog computing-based trust management and these techniques are classified into three categories: algorithms, challenges, and limitations. Further, it reviews the various trust attacks in fog environments, details the solutions proposed in the current literature, and concludes with a discussion of the open challenges and potential future research directions in fog computing. [ABSTRACT FROM AUTHOR]

This article aims to present the results of original empirical research on the diagnosis of factors predicting employee trust in managers in - Small and Medium Enterprises organizations undergoing the process of digitization. The concepts of digitization and trust are both defined and several determinants of trust in managers are established. The research methods used were a study of existing literature, as well as empirical research using quantitative methods, conducted using a questionnaire developed by the authors. The collected factual material indicates that the size of an organization is a variable affecting the level of trust between employees and managers in SME-sector organizations undergoing the process of digitization, and also affects which indicators of trust are professed by employees. The conducted research identified leading determinants of trust in managers in SME-sector organizations undergoing digitization. This article contributes to reducing a theoretical knowledge gap in the discussed area, while also indicating tools for optimizing the effectiveness of the human capital management process in a world dominated by modern technologies. Employees are a key element of modern organizations, and their success depends on the effectiveness of digital change management. Working in an environment based on trust has a positive effect on employees and thus determines the success of the entire organization. This article fills a research gap regarding the role of trust in relationship between employees and their superiors. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) vision has astoundingly transcended environmental sensing with integrated computing systems and smart devices, providing seamless connectivity among humans, machines, and their environment to cooperate for convenience and economical benefits. Apart from all the tremendous benefits of IoT, this paradigm still suffers from challenges of security and privacy vulnerabilities and demands a secure system for effective utilization of services in real-world IoT scenarios relying on which the IoT consumers expect secure and trustworthy communications. Trust Management (TM), which is a crucial aspect of security, plays a vital role in ensuring the exchange of information in a secure manner and maintaining the reliability of a system by measuring the degree of trust on IoT devices, reducing the uncertainties and risks involved in the systems. Thus, in recent years, Blockchain technology has been utilized for developing security innovations in TM field for different classes of IoT applications. It can provide tamper-proof data by enabling more reliable trust information and integrity verification, ultimately enhancing its availability and privacy when storing and sharing information. This paper provides a comprehensive survey that aims at analyzing and assessing Blockchain-based decentralized trust management systems (BCDTMS) for IoT. The contributions of this study are threefold; first, we provide the comprehensive and comparative analysis of state-of-the-art BCDTMS devised for different IoT classes such as Internet of Medical of Things (IoMT), Internet of Vehicles (IoV), Industrial IoT (IIoT), and Social IoT (SIoT). To make it an extensive study, we perform a detailed assessment of the existing BCDTMS in the literature in the aspects of Blockchain and TM. Second, we present requirements for developing Blockchain-based TM systems for IoT, and third we have highlighted the challenges in the context of using Blockchain for TM in various IoT applications. [ABSTRACT FROM AUTHOR]

Quality lifestyle leads to increasing trends in smart cities by offering modern communication and information technologies. Smart cities offer multiple applications with smart management of resources such as smart agriculture, Intelligent transportation systems, waste management and energy management. These applications are based on IoTs that are composed of sensor networks with limited processing and computing capabilities and are connected with different types of networks. Due to limited computational capability, IoT sensor nodes require more time to compute different tasks and are required to offload some tasks to remotely placed cloud servers for task execution. Fog nodes are preferred over the cloud as they are placed in close access to IoT nodes distributed in different networks. Different types of networks make it more vulnerable to malicious attacks. Malicious nodes offload complex and high computing tasks to fog nodes to compromise their performance and create delays in the computing tasks of legitimate nodes. In addition, fog nodes even after removing the malicious nodes are unable to process all the legitimate tasks within a specific time frame. In this work, a Trust-based Efficient Task Execution Scheme (TETES) is proposed for fog node that scrutinizes the offloaded tasks sent by the malicious nodes and efficiently execute most of the trusted tasks within a stipulated time cycle. The simulated results show that TETES execute more offloaded tasks as compared to well-known First Come First Serve (FCFS), Longest Task First (LTF), and Shortest Task First (STF) algorithms. [ABSTRACT FROM AUTHOR]

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

In 5G and beyond, the figure of cross-operator/domain connections and relationships grows exponentially among stakeholders, resources, and services, with reputation-based trust models being one of the capital technologies leveraged for trustworthy decision-making. This work studies novel 5G assets on which trust can be used to overcome unsuitable decision-making and address current requirements. First, it introduces a background and general architecture of reputation-based trust models. Then, it analyzes pivotal 5G assets on which trust can enhance their performance. In addition, this article performs a comprehensive review of the current reputation models applied to 5G assets and compares their properties, features, techniques, and results. Finally, it provides current trends and future challenges to conducting forthcoming research in the area. [ABSTRACT FROM AUTHOR]