Trust Evaluation Research Articles

ENHANCING SERVICE DISCOVERY IN MOBILE AD HOC NETWORKS USING SEMANTIC CLUSTERING AND HYBRID TRUST MANAGEMENT

Mobile Ad Hoc Networks (MANETs) facilitate communication among mobile devices without relying on fixed infrastructure. However, service discovery in MANETs faces challenges due to the dynamic topology and limited resources of nodes. Existing solutions often lack efficient resource utilization and fail to address trust management adequately. The conventional approaches for service discovery in MANETs are inefficient due to their inability to incorporate semantic clustering and robust trust management. Semantic clustering enhances the accuracy of service discovery by grouping nodes based on similar interests or functionalities. Meanwhile, traditional trust management mechanisms are inadequate in dynamic environments, leading to unreliable service discovery results. The proposed methodology involves developing a semantic clustering algorithm to organize nodes based on their semantic similarities. Additionally, a hybrid trust management system is implemented to assess the reliability of discovered services. The system combines both reputation-based and recommendation-based trust models to enhance the accuracy of trust evaluations. Through extensive simulations and experiments, the effectiveness of the proposed approach is demonstrated. The results indicate that the integration of semantic clustering and hybrid trust management significantly improves service discovery efficiency and reliability in MANETs. The approach achieves higher precision and recall rates compared to conventional methods, even under dynamic network conditions.

Hybrid machine learning approach for trust evaluation to secure MANET from routing attacks

A new era in communication has been ushered in by MANET networks, in which users (nodes) interact with one another through a self-configuring network of handheld devices linked by wireless links. Nodes are capable of participating and enthusiastic about sending packets to other nodes. Consequently, the need for a routing protocol materializes. The most difficult aspect is dealing with the network’s dynamic topology as a result of node mobility. This is because limited resources like storage space, battery life, and bandwidth require a protocol that can quickly adapt to topology changes while periodically updating messages. On the other hand, security is another important aspect of routing since the involvement of attackers will exhaust the network resources. This paper addresses the main issue of designing a routing protocol that handles all the adversaries and achieves better efficiency. For that, we proposed a Hybrid Machine Learning (HyML) model which evaluates the. Initially, the network is segregated by the Secure Stable Clustering (SSC) approach which first verifies the node’s legacy and forms clusters based on stability. The HyML is designed by combining two important ML techniques such as ANN and fuzzy-C Means (FCM) algorithm. The ANN model learns multiple attributes of the trust value and computes the cumulative trust score. Next, FCM determines the node position upon trust score. After the computation of the trust value, optimal route selection is performed by the Spider Monkey Optimization (SMO) technique. The overall work is evaluated through comprehensive simulations based on network longevity, throughput, energy usage, PDR, attack detection efficiency, and delay.

EDITORS: Energy-aware Dynamic Task Offloading using Deep Reinforcement Transfer Learning in SDN-enabled Edge Nodes

In mobile edge computing systems, a task offloading approach should balance efficiency, adaptability, trust management, and reliability. This approach aims to maximise resource utilisation, improve user experience, and satisfy application-specific requirements while taking into account the dynamic and limited resource nature of edge environments. Additionally, while offloading tasks, these systems are vulnerable to several attacks and privacy breaches, necessitating edge node trust evaluation. However, not all of these necessary features are present in the offloading methods currently used. This research proposes ‘EDITORS’ (energy-aware dynamic task offloading method utilising Deep Reinforcement Transfer Learning (DRTL) in Software-Defined Network (SDN) enabled edge computing environments), a novel approach aimed at addressing the multifaceted issues associated with offloading in mobile edge computing systems. The primary goal of EDITORS is to design a task offloading system that incorporates trusted edge nodes while prioritising energy efficiency, timeliness, reliability, adaptability, and outperforming existing task offloading methods in terms of the quality of the task offloading plan. This method uses of DRTL agents at edge nodes, which communicate with the SDN controller to learn the most appropriate offloading choices based on network conditions and resource availability. Extensive simulations (six) are conducted which show that the EDITORS significantly increases energy efficiency while preserving low-latency task completion compared to five existing offloading methods (DDLO, DROO, DMRO, DRL without TL and SDN and DRL with SDN). EDITORS includes trust evaluation, trusted edge device prediction using LSTM, and adaptation of newly added devices through transfer learning, unlike other task offloading methods that just concentrate on task offloading.

Human-computer interaction: Evaluation of trust and emotional

With the advancement of information technology, human-computer interaction is becoming increasingly intelligent. This progress has led to the transformation of the car cabin from a simple driving space to an intelligent mobile life terminal. At the same time, more and more people have psychological problems in modern society, and timely adjustment of their negative emotions is of great practical significance to social harmony and stability. Traditional methods of emotion regulation require a lot of manpower and energy. Hence, this paper presents an approach that utilizes text dialogue and somatosensory interaction to detect the users emotions and subsequently adjust them. By examining and analyzing the current research landscape, the objective of this study is to offer insights for designing trustworthy and emotionally-responsive interactions in the automotive context. It can bring a better driving experience to smart car users and bring benefits to many companies. It is of great scientific and application value to carry out this research in depth to promote the development of human-computer interaction. Through literature review, case study, theoretical analysis and other research methods. This paper sorts out the establishment of trust degree and recognition of emotional changes in human-computer interaction, and puts forward the role, defects and improvements of trust establishment in human-car cooperation, as well as the reasons affecting user emotions, and how to improve the defects of the application of voice emotion recognition. It also discusses difficulties in establishing trust and misperception of emotions.

Cross-National Study of Transparency in CSR Communication and Corporate Trust: Mediating Roles of Perceived Altruism and Perceived Skepticism

This study uses online surveys administered in the US ( N = 491), the UAE ( N = 538), and South Korea ( N = 548) to examine the associations between transparency strategies in CSR communication (i.e., participation, substantial information, and accountability) and the public’s evaluation of corporate trust. Additionally, it investigates the mediating roles of perceived altruism and skepticism toward CSR, while also exploring how these associations differ across the three selected countries. When testing a proposed model using the combined data from three countries ( N = 1,577), our findings suggest that transparency strategies do not directly contribute to corporate trust. However, the participation aspect leads to corporate trust only through the influence of perceived altruism, while substantial information predicts a reduced level of CSR skepticism. Notably, results derived from country-specific data reveal intriguing patterns concerning accountability. In the US, accountability leads to altruism and fosters corporate trust, while in the UAE, it contributes to fostering corporate trust. However, in South Korea, it leads to increased skepticism. Conversely, in South Korea, substantial information contributes to building corporate trust. We discuss the implications of these findings from the unique institutional, social, and cultural perspectives of the countries studied.

A public static agreement key based cryptography for secure data transmission in WSN based smart environment application

Wireless Sensor Networks (WSN) is an emerging networking technology that allows for the low-cost, unattended monitoring of a variety of environments, thus WSN technologies have become increasingly important. Simultaneously, lack of security and poor energy efficiency is considered the major concerns in many routing protocols in various existing WSN protocols. Security and energy efficiency concerns are mainly due to the emergence of various kinds of malicious attacks because of advancements in networking technology. To mitigate these issues secured transmission is essential in any routing protocol. In order to provide safe data transfer in WSN-based smart environment applications, the TABEESR protocol (Trust Analysis Based Energy Efficient and Safe Routing) was developed. The proposed routing protocol encompasses four phases such as network creation and node deployment, grading for trust evaluation, route identification process and data transmission. The trust evaluation is performed with the aid of sensor packet features to categorize the positive nodes and negative nodes. The path identification process includes two processes such as cluster head selection through red fox optimization and relay node selection using a fuzzy interference system for energy efficient and secure path selection. A public static agreement key based cryptography technique is introduced for secure data transmission. The proposed TABEESR is tested with MATLAB software to validate its performance. SMEER, SHER, SRPA and AFSA are the prior routing protocols in WSN that are considered to compare the performance of the proposed routing protocol. The proposed TABEESR algorithm attained 95(%) of Average Residual Energy, 91 (%) of packet delivery ratio, 1350 (sec) network lifetime, 0.131 (Mbps) of throughput and 7(%) Packet Loss. Moreover, the proposed PSAKC algorithm attained 5.8 (%) information loss, 3.8 (sec) encryption time, 2.3 (sec) decryption time, 6 (sec) execution time, and 6.5 (J) energy consumption which is better than the prior methods. The proposed routing algorithm is applicable for providing security in both server to device and device to device in various smart applications.

Retracted: Fault-Tolerant Secure Routing Based on Trust Evaluation Model in Data Center Networks

Retracted: Fault-Tolerant Secure Routing Based on Trust Evaluation Model in Data Center Networks

EEG-based assessment of driver trust in automated vehicles

Effective collaboration between automated vehicles (AVs) and human drivers relies on maintaining an appropriate level of trust. However, real-time assessment of human trust remains a significant challenge. While initial efforts have delved into the potential use of physiological signals, such as skin conductance and heart rate, to evaluate trust, limited attention has been given to the feasibility of assessing trust through electroencephalogram (EEG) signals. This study aimed to address this issue by using EEG signals to objectively assess driver trust towards AVs. A simulated driving experiment was conducted, where driver trust was manipulated by introducing different types of AV malfunctions. Self-reported trust ratings were collected and used to classify driver trust into three levels: low, medium, and high. A total of 420 time- and frequency-domain EEG features were extracted, and nine machine learning algorithms were applied to construct driver trust assessment models. Additionally, to explore the potential of developing cost-effective models with reduced feature inputs, this study developed trust models using features solely from single brain regions: frontal, parietal, occipital, or temporal. The results showed that the best-performing model, utilizing features from the whole brain and employing the Light Gradient Boosting Machine (LightGBM) algorithm, achieved an accuracy of 88.44% and an F1-score of 78.31%. In comparison, models based on single brain regions did not achieve comparable performance to the comprehensive model. However, the frontal and parietal regions showed important potentials for developing cost-effective trust assessment models. This study also performed feature analysis on the best-performing model to identify features highly responsive to changes in trust. The results showed that an increased power of beta waves tended to indicate a lower level of trust in AVs. These findings contribute to our understanding of the neural correlates of trust in AVs and hold practical implications for the development of trust-aware AV technologies capable of adapting and responding to driver's trust levels effectively.

Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions.

The Internet of Vehicles (IoV) is a technology that is connected to the public internet and is a subnetwork of the Internet of Things (IoT) in which vehicles with sensors are connected to a mobile and wireless network. Numerous vehicles, users, things, and networks allow nodes to communicate information with their surroundings via various communication channels. IoV aims to enhance the comfort of driving, improve energy management, secure data transmission, and prevent road accidents. Despite IoV's advantages, it comes with its own set of challenges, particularly in the highly important aspects of security and trust. Trust management is one of the potential security mechanisms aimed at increasing reliability in IoV environments. Protecting IoV environments from diverse attacks poses significant challenges, prompting researchers to explore various technologies for security solutions and trust evaluation methods. Traditional approaches have been employed, but innovative solutions are imperative. Amid these challenges, machine learning (ML) has emerged as a potent solution, leveraging its remarkable advancements to effectively address IoV's security and trust concerns. ML can potentially be utilized as a powerful technology to address security and trust issues in IoV environments. In this survey, we delve into an overview of IoV and trust management, discussing security requirements, challenges, and attacks. Additionally, we introduce a classification scheme for ML techniques and survey ML-based security and trust management schemes. This research provides an overview for understanding IoV and the potential of ML in improving its security framework. Additionally, it provides insights into the future of trust and security enhancement.

The development of the Epistemic Trust Rating System (ETRS)

Objective The present study introduces and validates the Epistemic Trust Rating System (ETRS), an observer-based measure designed to assess epistemic trust (ET) within psychotherapy. ET in psychotherapy has gained much theoretical attention as a critical component in the therapeutic context, given its inherent link to social communication. However, its empirical validation remains pending, largely due to the absence of a refined instrument to gauge ET levels within the therapy environment. Therefore, this study aimed to translate ET’s theoretical construct into tangible markers within the therapeutic context. Method One hundred eighteen patients enrolled in a randomized controlled trial received psychodynamic psychotherapy for depression. Incorporating top-down theoretical considerations with bottom-up empirical observations yielded an overall ETRS score accompanied by three distinct sub-scales, each assessing a singular ET element: one gauges the patient’s propensity to share; another measures the degree to which “we-mode” moments are achieved within the session; and the third evaluates the patient’s receptiveness to learning. Results The findings demonstrate the psychometric robustness of the ETRS, with good internal consistency, interrater reliability (ICC(1,8) = .86-.90), and convergent (r = .23-.29) and discriminant validity (r = -.10). Conclusion The current study highlights the ETRS as a promising tool bridging theory and empirical exploration, enhancing our understanding of epistemic trust in psychotherapy.

Engaging with artificial intelligence in mammography screening: Swedish breast radiologists' views on trust, information and expertise.

Lack of trust and transparency is stressed as a challenge for clinical implementation of artificial intelligence (AI). In breast cancer screening, AI-supported reading shows promising results but more research is needed on how medical experts, which are facing the integration of AI into their work, reason about trust and information needs. From a sociotechnical information practice perspective, we add to this knowledge by a Swedish case study. This study aims to: (1) clarify Swedish breast radiologists' views on trust, information and expertise pertaining to AI in mammography screening and (2) analytically address ideas about medical professionals' critical engagement with AI and motivations for trust in AI. An online survey was distributed to Swedish breast radiologists. Survey responses were analysed by descriptive statistical method, correlation analysis and qualitative content analysis. The results were used as foundation for analysing trust and information as parts of critical engagements with AI. Of the Swedish breast radiologists (n = 105), 47 answered the survey (response rate = 44.8%). 53.2% (n = 25) of the respondents would to a high/somewhat high degree trust AI assessments. To a great extent, additional information would support the respondents' trust evaluations. What type of critical engagement medical professionals are expected to perform on AI as decision support remains unclear. There is a demand for enhanced information, explainability and transparency of AI-supported mammography. Further discussion and agreement are needed considering what the desired goals for trust in AI should be and how it relates to medical professionals' critical evaluation of AI-made claims in medical decision support.

Grassroots and Digital Outreach Strategies Raise Awareness of COVID-19 Information and Research in Baltimore City.

We aimed to disseminate reliable COVID-19 information to the Black and Latino communities of Baltimore City, Maryland, between July 2020 and December 2022. With community partners, we disseminated evidence-based COVID-19 information via grassroots and digital strategies, including Hopkins Opportunity for Participant Engagement, and connected volunteers to COVID-19 research. Using a multimodal approach facilitated dissemination of reliable information and raised awareness of research; evaluation of trust is ongoing. Robust, multimodal strategies are needed to foster trust and equity among diverse communities. (Am J Public Health. 2024;114(S1):S69-S73. https://doi.org/10.2105/AJPH.2023.307492).

Secure multi-path routing for Internet of Things based on trust evaluation.

In the realm of the Internet of Things (IoT), ensuring the security of communication links and evaluating the safety of nodes within these links remains a significant challenge. The continuous threat of anomalous links, harboring malicious switch nodes, poses risks to data transmission between edge nodes and between edge nodes and cloud data centers. To address this critical issue, we propose a novel trust evaluation based secure multi-path routing (TESM) approach for IoT. Leveraging the software-defined networking (SDN) architecture in the data transmission process between edge nodes, TESM incorporates a controller comprising a security verification module, a multi-path routing module, and an anomaly handling module. The security verification module ensures the ongoing security validation of data packets, deriving trust scores for nodes. Subsequently, the multi-path routing module employs multi-objective reinforcement learning to dynamically generate secure multiple paths based on node trust scores. The anomaly handling module is tasked with handling malicious switch nodes and anomalous paths. Our proposed solution is validated through simulation using the Ryu controller and P4 switches in an SDN environment constructed with Mininet. The results affirm that TESM excels in achieving secure data forwarding, malicious node localization, and the secure selection and updating of transmission paths. Notably, TESM introduces a minimal 12.4% additional forwarding delay and a 5.46% throughput loss compared to traditional networks, establishing itself as a lightweight yet robust IoT security defense solution.

An evaluation of customer trust in e-commerce market based on entropy weight analytic hierarchy process

An evaluation of customer trust in e-commerce market based on entropy weight analytic hierarchy process

Mind Drifts, Data Shifts: Utilizing Mind Wandering to Track the Evolution of User Experience with Data Visualizations.

User experience in data visualization is typically assessed through post-viewing self-reports, but these overlook the dynamic cognitive processes during interaction. This study explores the use of mind wandering- a phenomenon where attention spontaneously shifts from a primary task to internal, task-related thoughts or unrelated distractions- as a dynamic measure during visualization exploration. Participants reported mind wandering while viewing visualizations from a pre-labeled visualization database and then provided quantitative ratings of trust, engagement, and design quality, along with qualitative descriptions and short-term/long-term recall assessments. Results show that mind wandering negatively affects short-term visualization recall and various post-viewing measures, particularly for visualizations with little text annotation. Further, the type of mind wandering impacts engagement and emotional response. Mind wandering also functions as an intermediate process linking visualization design elements to post-viewing measures, influencing how viewers engage with and interpret visual information over time. Overall, this research underscores the importance of incorporating mind wandering as a dynamic measure in visualization design and evaluation, offering novel avenues for enhancing user engagement and comprehension.

Trustworthy Distributed Average Consensus based on Locally Assessed Trust Evaluations

Trustworthy Distributed Average Consensus based on Locally Assessed Trust Evaluations

A comprehensive decision assessment for trust evaluation in wireless sensor networks

Objective: This study endeavors to tackle the pressing issue of network security within Wireless Sensor Networks (WSNs) through the introduction of an innovative integrated Pythagorean fuzzy-based method for evaluating network nodes. The primary aim is to enrich decision making frameworks within WSNs by furnishing a robust methodology for identifying and eradicating malevolent nodes. Methods: investigation employs an integrated Pythagorean fuzzy-based methodology to appraise network nodes by scrutinizing specific trust attributes. Comparative assessment with alternative contemporary Multiple Criteria Decision Making (MCDM) trust models is conducted to gauge the effectiveness of the proposed strategy. Results: The results underscore the efficacy of the integrated Pythagorean fuzzy-based approach in bolstering network security through the precise identification and elimination of malicious nodes. Comparative analysis with other MCDM trust models highlights the superiority of the proposed approach in evaluating network nodes based on trust attributes. Recommendations: In light of the findings, it is advised to integrate fuzzy decision analysis methodologies into decision making systems for WSNs to enhance network security. Additionally, future research endeavors could concentrate on refining and expanding upon the proposed methodology to effectively address emerging security challenges within WSNs.

Trustworthy-Based Authentication Model with Intrusion Detection for IoT-Enabled Networks with Deep Learning Algorithm

In the burgeoning field of the Internet of Things (IoT), ensuring secure and trustworthy communication between devices is paramount. This paper proposes a novel Trustworthy-Based Authentication Model (TBAM) integrated with Intrusion Detection Systems (IDS) leveraging deep learning algorithms to secure IoT-enabled networks. The proposed model addresses the dual challenges of authenticating legitimate devices and detecting malicious intrusions. Specifically, we employ a Convolutional Neural Network (CNN) to analyse network traffic patterns for intrusion detection, leveraging its prowess in feature extraction and classification. Additionally, a Long Short-Term Memory (LSTM) network is utilized for continuous monitoring and anomaly detection, capturing temporal dependencies in data flows that are indicative of potential security threats. The authentication mechanism integrates a trust evaluation system that assigns trust scores to devices based on their behaviour, enhancing the model's capability to distinguish between trusted and malicious entities. Our extensive experiments on real-world IoT datasets demonstrate that the TBAM significantly outperforms traditional security models in terms of detection accuracy, false-positive rate, and computational efficiency. Specifically, our model achieves a detection accuracy of 98.7%, a false-positive rate of 1.2%, and a processing time reduction of 30% compared to baseline models. This work contributes a robust, scalable, and efficient solution to the pressing security concerns in IoT networks, paving the way for more secure and reliable IoT applications.

A Proactive Trust Evaluation System for Secure Data Collection Based on Sequence Extraction

A Proactive Trust Evaluation System for Secure Data Collection Based on Sequence Extraction

Building a Comprehensive Trust Evaluation Model to Secure Cloud Services From Reputation Attacks

Building a Comprehensive Trust Evaluation Model to Secure Cloud Services From Reputation Attacks