Trust Model Research Articles

Sign Language Detection without Sensors

bstract: The time period time device assists communication between the hearing impaired and the general public. We are proud to present the design and implementation of language learning models. In most cases, we create a trust model that distributes signatures regularly. Also, this method is a good way for beginners to use sign language to practice the language. As part of this study, various human-computer interactions for gesture recognition were investigated and analyzed. It has been found that the best way to detect human movement is layered image processing. Even with very little background, the system can detect certain characters in the language with 70-80% accuracy.

Trust and Risk Assessment in IoT Networks

The Internet of Things (IoT) is a large-scale, heterogeneous ecosystem of connected devices encompassing a range of purposes and computing capabilities. As IoT systems grow ubiquitous, new approaches to security are needed. This work proposes a method of risk assessment for devices that combines the use of trust models based on dynamic behaviors with static capability profiles drawn from immutable device characteristics to determine the level of risk each device poses to network security. A risk-based approach allows security mechanisms and monitoring activities to be more efficiently allocated across IoT networks. Simultaneously, devices can be allowed a greater degree of functionality while ensuring system availability and security. This paper presents a methodology and architecture to integrate risk assessment into IoT networks. This allows additional tailoring of security control application and provides higher-level, more human-readable information for security analysts.

A trust management-based secure routing protocol with AUV-aided path repairing for Underwater Acoustic Sensor Networks

Underwater Acoustic Sensor Networks (UASNs) are widely applied in marine based applications, but traditional security mechanisms are not feasible due to the underwater sensor nodes’ limited constraints and capabilities. Moreover, few existing routing protocols for underwater sensor networks considered underwater transmision limited constraints. In this paper, we propose T-SAPR, a trust management-based secure routing protocol with AUV-aided path repairing that addresses these challenges. T-SAPR uses an attention-based long short-term memory (LSTM) to build a trust model that considers node trust, communication trust, and environment surrounding’s trust. T-SAPR evaluates trust evidence to measure the trust level of the sensor nodes in the network and uses energy, packet delivery ratio, and latency jointly to seek optimal routing policies in the reward function of Q-Learning. Additionally, we propose a repairing mechanism with the assistance of AUVs to improve the reliability of UASNs in cases of multiple sensor node failures or the detection of malicious nodes. Evaluation results show that T-SAPR identifies malicious nodes by detecting their anomalous behavior while improving the packet delivery ratio and energy efficiency by 13.3% and 48.1% as compared to existing algorithms such as GEDAR, TADR-EAODV, and AFSA-ACOA-SCP.

BLAST-IoT: BLockchain Assisted Scalable Trust in Internet of Things

Internet of Things (IoT) paved the way to realize ubiquitous access towards manifold services and applications by enabling connectivity between different physical devices. The devices, having heterogeneous computation power, protocol stack, or storage power, can send or receive various services by communicating through the internet. These services inevitably demand a secure and reliable network system to interchange the data or information for their impressive utilization. One proven solution to attain this objective is embedding Trust Management System (TMS) that aims to reduce risk or uncertainty associated with interacting adversary devices by capturing their dynamic behavior in the system. With TMS, devices compute and maintain trust value on the interacting devices by observing their behavior with different interaction instances. This trust information is exchanged between the devices to assess the global reputation of devices and facilitate devices’ decisions when there is a lack of direct interactions. For systems having enormous low-power devices, one crucial aspect of TMS is secure and timely dissemination, management, and storage of trust information to accurately assess devices’ dynamic behavior. Addressing this issue, we present a blockchain-assisted scalable trust model, BLAST-IoT, for IoT systems of heterogeneous devices in this paper. BLAST-IoT intends to ensure secure dissemination and storage of trust information leveraging on the functionalities provided by blockchain technology. It adopts a semi-distributed approach where direct trust information, computed by IoT end devices, is processed and stored by some high-power edge devices running public blockchain networks to handle impediments caused by power-deficient IoT devices. The effectiveness of BLAST-IoT has been evaluated through implementation and simulation-based results for trust convergence and assessment accuracy in the dynamic IoT environment. Further, the throughput performance of BLAST-IoT has been examined with an increase in network load.

An optimal trust and secure model using deep metric learning for fog‐based VANET

AbstractVehicular ad hoc network (VANET) is a technique that enhances road safety via communication between two different vehicles. Trust establishment in VANET in between the vehicles is the most significant factor that provides secure reliability and integrity. Reliability and trust assist the vehicles in gathering credible data from neighboring vehicles. On the other hand, incomplete and inaccurate data gathered by the vehicles contain interrupting effect on VANET. Therefore, this article proposes a modified deep metric learning‐based chaotic Henry gas solubility optimization (MDML‐CHGSO) approach to provide a trust‐based security design. The proposed trust model is based on plausibility as well as experience in securing vehicle networks. The proposed model verifies the received data's genuineness from the approved vehicle using various processes namely, authentication, lifetime verification, experience evaluation, plausibility determination, and accurate measurement. The adoption of the modified DML‐based CHGSO approach extracts the location of the event message accurately when the location of the message is located closer to the fog nodes. In addition to this, average latency, throughput and packet delivery ratio, are the metrics evaluated for rating the performance.

(Retracted) Cyber-security trust model through adaptive cloud authentication protocol for web application

(Retracted) Cyber-security trust model through adaptive cloud authentication protocol for web application

A qualitative study on COVID-19 pandemic impacts on parental attitudes and intentions for routine adolescent vaccinations: The role of trust

IntroductionThe COVID-19 pandemic has contributed to declines in routine childhood and adolescent vaccination coverage globally. While the declines in Australia have been less, they are a concern, given steady increases in coverage prior to the pandemic. Given limited evidence on how the experiences of parents during the pandemic affected their attitudes about and intentions towards adolescent vaccinations, with this study we aimed to explore these. MethodsThis was a qualitative study. We invited parents of adolescents eligible for school-based vaccinations in 2021 from metropolitan, regional and rural areas of New South Wales and Victoria (the most affected States) and South Australia (less affected) to half hour-long online semi-structured interviews. We analysed data thematically and applied a conceptual model of trust in vaccination. ResultsIn July 2022 we interviewed 15 accepting, 4 hesitant and two parents who refused adolescent vaccinations. We identified three themes: 1. Pandemic impacting on professional and personal lives and routine immunisations; 2. Pandemic strengthening preexisting vaccine hesitancy, with perceived lack of clarity in governmental information about vaccination and stigma around non-vaccinating as contributing factors; 3. Pandemic raising awareness of the benefits of COVID-19 and routine vaccinations, with communication campaigns and one’s trusted doctor’s vaccination recommendations as contributing factors. ConclusionsFor some parents, experiences of poor system readiness and growing distrust towards health and vaccination systems strengthened their pre-existing vaccine hesitancy. We offer recommendations on how trust in the health system and immunisation can be optimised post-pandemic to increase uptake of routine vaccines. These include improving access to vaccination services and clear, timely information about vaccines; supporting immunisation providers in their immunisation consultations; working alongside communities, and building capacity of vaccine champions.

Fully Homomorphic Encryption with Optimal Key Generation Secure Group Communication in Internet of Things Environment

The Internet of Things or “IoT” determines the highly interconnected network of heterogeneous devices where each type of communication seems to be possible, even unauthorized. Consequently, the security requirement for these networks became crucial, while conventional Internet security protocol was identified as unusable in these types of networks, especially because of some classes of IoT devices with constrained resources. Secure group communication (SGC) in the IoT environment is vital to ensure the confidentiality, integrity, and availability (CIA) of data swapped within a collection of IoT devices. Typically, IoT devices were resource-constrained with limited memory, processing, energy, and power, which makes SGC a difficult task. This article designs a Fully Homomorphic Encryption with Optimal Key Generation Secure Group Communication (FHEOKG-SGC) technique in the IoT environment. The presented FHEOKG-SGC technique mainly focuses on the encryption and routing of data securely in the IoT environment via group communication. To accomplish this, the presented FHEOKG-SGC technique initially designs an FHE-based encryption technique to secure the data in the IoT environment. Next, the keys in the FHE technique are chosen optimally using the sine cosine algorithm (SCA). At the same time, the plum tree algorithm (PTA) is applied for the identification of the routes in the IoT network. Finally, the FHEOKG-SGC technique employs a trust model to improve the secure communication process, and the key management center is used for optimal handling of the keys. The simulation analysis of the FHEOKG-SGC technique is tested using a series of experiments, and the outcomes are studied under various measures. An extensive comparative study highlighted the improvement of the FHEOKG-SGC algorithm over other recent approaches.

Formal verification of fraud-resilience in a crowdsourcing consensus protocol

Crowdsourcing has emerged as a promising computing paradigm that utilizes human intelligence to achieve complex tasks, but it encounters several security and trust issues. Blockchain is a potential technology that can resolve most of these issues, however, it is difficult to find an appropriate consensus protocol applicable to crowdsourcing systems. Therefore, this work presents a Trust and Transactions Chain (TTC) consensus protocol built upon blockchain technology. It selects a trusted leader and validators considering a trust model which depends on deposit ratio, block generation and validation rate, and waiting rate. The TTC protocol addresses the main challenge of ensuring correctness related to critical systems of crowdsourcing which has extreme significance as their failure can result in disastrous consequences. This work is primarily focused on fraud-resilience avoiding double-spending attack. It also deals with sybil and eclipse attacks. Model checking is exploited because it is effective and automatic to conduct formal verification. The TTC protocol is formally modeled utilizing Communicating Sequential Programs, and the fraud-resilience property is specified using Linear Temporal Logic. The verification of the model is done using Process Analysis Toolkit that takes the formal model and specified properties as input to inspect the properties’ satisfaction or violation. The results of the formal verification are analyzed with respect to the verification time and the number of visited states.

A Double-Layer Blockchain Based Trust Management Model for Secure Internet of Vehicles

The Internet of Vehicles (IoV) enables vehicles to share data that help vehicles perceive the surrounding environment. However, vehicles can spread false information to other IoV nodes; this incorrect information misleads vehicles and causes confusion in traffic, therefore, a vehicular trust model is needed to check the trustworthiness of the message. To eliminate the spread of false information and detect malicious nodes, we propose a double-layer blockchain trust management (DLBTM) mechanism to objectively and accurately evaluate the trustworthiness of vehicle messages. The double-layer blockchain consists of the vehicle blockchain and the RSU blockchain. We also quantify the evaluation behavior of vehicles to show the trust value of the vehicle’s historical behavior. Our DLBTM uses logistic regression to accurately compute the trust value of vehicles, and then predict the probability of vehicles providing satisfactory service to other nodes in the next stage. The simulation results show that our DLBTM can effectively identify malicious nodes, and over time, the system can recognize at least 90% of malicious nodes.

Mitigation of Black Hole and Selective Forwarding Attacks through Active Trust Mechanisms in Wireless Sensor Networks

In modern times, security remains a significant concern in wireless networks. Wireless sensor networks are widely employed in different practical situations. WSNs struggle with numerous internal and external threats, and detecting and defending against insider attacks is difficult. Insider attacks, in which intruders discard received data packets selectively, pose a significant risk to WSNs. The presence of black hole nodes within the network causes this issue. This research paper presents a secure and reliable route management methodology for WSNs that selects nodes and routing paths based on trust metrics. The proposed mechanism employs intelligent routing and node selection to defend against various types of assaults encountered during the routing process, including selective forwarding and black hole attacks. As a result, the paper identifies secure routes and establishes secure network routing. This scheme uses throughput, latency, and packet drop ratio as performance metrics. Experimentation demonstrates that the proposed system effectively increases the probability of secure network routing paths while extending the network lifecycle.

Blockchain-Assisted Reputation Management Scheme for Internet of Vehicles

With the rapid development of Internet of Vehicles (IoV), particularly the introduction of Mobile Edge Computing (MEC), vehicles can efficiently share data with one another. However, edge computing nodes are vulnerable to various network attacks, posing security risks to data storage and sharing. Moreover, the presence of abnormal vehicles during the sharing process poses significant security threats to the entire network. To address these issues, this paper proposes a novel reputation management scheme, which proposes an improved multi-source multi-weight subjective logic algorithm. This algorithm fuses the direct and indirect opinion feedback of nodes through the subjective logic trust model while considering factors such as event validity, familiarity, timeliness, and trajectory similarity. Vehicle reputation values are periodically updated, and abnormal vehicles are identified through reputation thresholds. Finally, blockchain technology is employed to ensure the security of data storage and sharing. By analyzing real vehicle trajectory datasets, the algorithm is proven to effectively improve the differentiation and detection rate of abnormal vehicles.

A trust, privacy and security model for e-commerce in Nigeria

Today, digital technologies has permeated every sector of human life from communication to medicine to education to transportation and most recently shopping.. The mode of buying and selling by Nigerians have moved from traditional to digital, whereby consumers buy and sell goods online. The online presence of commerce is known as Electronic commerce or E-commerce and it is an initiative amongst other emerging digital sectors in Nigeria that has witnessed substantive growth recently. However, the issues of security, privacy of data and the trust endeared by users on the initiative remains a challenging one. Presently in Nigeria, the solutions to combat security concerns of E-commerce are technically inclined and therefore do not sufficiently capture the solution paradigm. Due to a rise in these E-commerce‘s security-related concerns in Nigeria as discovered by taking random samples of the population used for the survey, this research proposes a Security, Trust and Privacy model for the survival of E-commerce in Nigeria.

Machine autonomy for rehabilitation of elderly people: A trade-off between machine intelligence and consumer trust

This is an exploratory study with the objective to understand elderly people’s expected behavior to accept and use machine autonomy controlled by ambient intelligence. It was conducted on a proposed theoretical framework, the expected trust model for machine autonomy (ETM4MA). A detailed survey was collected among elderly people living at home with their family members in a developing country, Bangladesh. Based on the results obtained from structural equation modeling (SEM) on the sample, it was observed that elderly people’s acceptance of automation systems driven by ambient intelligence as a substitute for caregiving support directly from human beings was linked to their concerns about emotional belongingness and social interaction as well as their ability to use the technology. Thus, in developing trust in this machine autonomy, their expectations around personal ability and the feeling of caring are two important issues for elderly people. To form the behavioral intention to accept this machine autonomy, trust plays a crucial role; however, this might vary depending on the differences in personality and behavioral attitude.

Governing fiduciary relationships or building up a governance model for trust in AI? Review of healthcare as a socio-technical system

ABSTRACT ‘: Fiduciary law aims to mitigate the inherent risk of ‘trust’, which helps restore interpersonal trust. It remains to be answered how trust should be governed in an AI-driven socio-technical system where technical and social factors are involved including interpersonal relationships and AI-human interactions. Taking interpersonal trust as the backdrop of analysis, this article seeks answers to this question focusing on healthcare. It firstly draws a conceptual framework regarding 'trust' and investigates its interplay with AI as well as examines how it is governed under the fiduciary law. Subsequently, it upholds a socio-technical system perspective, examining how to enable and sustain trust in an AI-driven socio-technical system. A governance model is then developed to elicit ‘intrinsic’, ‘dynamic’ and ‘ethical’ values of trust attributed to various elements under a tri-partite framework. It is recognised that findings of the literature as to trust, its trajectory and implications can be implemented within the proposed framework. Furthermore, it brings novelty by re-conceptualising the elements of 'trust' and associated values, marking distinction to its interpersonal roots and fiduciary relationships. It is considered this governance model, by upholding a holistic viewpoint, provides a generalisable framework that can construct, maintain and restore trust in AI-driven socio-technical systems.

A probabilistic trust model for cloud services using Bayesian networks

With the industry’s fast adoption of cloud computing, it’s been hard to keep trust between cloud service providers and people who use their services. In fact, trust management in cloud computing has become very challenging given the urgent need for cloud service requesters to choose efficient, trustworthy, and non-risky services. One of the most important factors that can be considered in the applicant’s trust or distrust of a service is the different quality of services related to the service. Therefore, approaches are needed to assess the trustworthiness of cloud services with respect to their quality of service (QoS). Given the uncertainty that exists for cloud services, it is more realistic to model their QoS parameters as random variables and also consider different dependencies between them. In this paper, a new trust model for cloud services is proposed using a Bayesian network, which is a probabilistic graphical model that can be used as one of the best methods to control uncertainty. Using Bayesian networks makes it possible to infer more accurate QoS values, which leads to the selection of highly trustworthy services by several cloud service requesters. To evaluate the proposed trust model, experimental results are performed using a real-world cloud service quality feedback, collected by the $$CloudArmor$$ project. The results of the experiments show that the proposed trust model is highly accurate and significantly reduces the estimation error.

A quantum trust and consultative transaction-based blockchain cybersecurity model for healthcare systems

Many researchers have been interested in healthcare cybersecurity for a long time since it can improve the security of patient and health record data. As a result, a lot of research is done in the field of cybersecurity that focuses on the safe exchange of health data between patients and the medical setting. It still has issues with high computational complexity, increased time consumption, and cost complexity, all of which have an impact on the effectiveness and performance of the complete security system. Hence this work proposes a technique called Consultative Transaction Key Generation and Management (CTKGM) to enable secure data sharing in healthcare systems. It generates a unique key pair based on random values with multiplicative operations and time stamps. The patient data is then safely stored in discrete blocks of hash values using the blockchain methodology. The Quantum Trust Reconciliation Agreement Model (QTRAM), which calculates the trust score based on the feedback data, ensures reliable and secure data transfer. By allowing safe communication between patients and the healthcare system based on feedback analysis and trust value, the proposed framework makes a novel contribution to the field. Additionally, during communication, the Tuna Swarm Optimization (TSO) method is employed to validate nonce verification messages. Nonce message verification is a part of QTRAM that helps verify the users during transmission. The effectiveness of the suggested scheme has been demonstrated by comparing the obtained findings with other current state-of-the-art models after a variety of evaluation metrics have been analyzed to test the performance of this security model.

A Hybrid Trust Model against Insider Packet Drop Attacks in Wireless Sensor Networks.

Quick and accurate detection of inside packet drop attackers is of critical importance to reduce the damage they can have on the network. Trust mechanisms have been widely used in wireless sensor networks for this purpose. However, existing trust models are not effective because they cannot distinguish between packet drops caused by an attack and those caused by normal network failure. We observe that insider packet drop attacks will cause more consecutive packet drops than a network abnormality. Therefore, we propose the use of consecutive packet drops to speed up the detection of inside packet drop attackers. In this article, we describe a new trust model based on consecutive drops and develop a hybrid trust mechanism to seamlessly integrate the new trust model with existing trust models. We perform extensive OPNET (Optimized Network Engineering Tool) simulations using a geographic greedy routing protocol to validate the effectiveness of our new model. The simulation results show that our hybrid trust model outperforms existing trust models for all types of inside packet drop attacks, not only in terms of detection speed and accuracy as it is designed for, but also in terms of other important network performance metrics, such as packet delivery rate, routing reliability, and energy efficiency.

RETRACTED ARTICLE: Machine learning in cloud-based trust modeling in M-health application using classification with image encryption

RETRACTED ARTICLE: Machine learning in cloud-based trust modeling in M-health application using classification with image encryption

VANET security enhancement in cloud navigation with Internet of Things-based trust model in deep learning architecture

VANET security enhancement in cloud navigation with Internet of Things-based trust model in deep learning architecture