Trust Model Research Articles

Trust Metric-Based Anomaly Detection via Deep Deterministic Policy Gradient Reinforcement Learning Framework

Addressing real-time network security issues is paramount due to the rapidly expanding IoT jargon. The erratic rise in usage of inadequately secured IoT- based sensory devices like wearables of mobile users, autonomous vehicles, smartphones and appliances by a larger user community is fuelling the need for a trustable, super-performant security framework. An efficient anomaly detection system would aim to address the anomaly detection problem by devising a competent attack detection model. This paper delves into the Deep Deterministic Policy Gradient (DDPG) approach, a promising Reinforcement Learning platform to combat noisy sensor samples which are instigated by alarming network attacks. The authors propose an enhanced DDPG approach based on trust metrics and belief networks, referred to as Deep Deterministic Policy Gradient Belief Network (DDPG-BN). This deep-learning-based approach is projected as an algorithm to provide “Deep-Defense” to the plethora of network attacks. Confidence interval is chosen as the trust metric to decide on the termination of sensor sample collection. Once an enlisted attack is detected, the collection of samples from the particular sensor will automatically cease. The evaluations and results of the experiments highlight a better detection accuracy of 98.37% compared to its counterpart conventional DDPG implementation of 97.46%. The paper also covers the work based on a contemporary Deep Reinforcement Learning (DRL) algorithm, the Actor Critic (AC). The proposed deep learning binary classification model is validated using the NSL-KDD dataset and the performance is compared to a few deep learning implementations as well.

Flamingo Jelly Fish search optimization-based routing with deep-learning enabled energy prediction in WSN data communication

ABSTRACT Nowadays, wireless sensor networks (WSN) have gained huge attention worldwide due to their wide applications in different domains. The limited amount of energy resources is considered as the main limitations of WSN, which generally affect the network life time. Hence, a dynamic clustering and routing model is designed to resolve this issue. In this research work, a deep-learning model is employed for the prediction of energy and an optimization algorithmic technique is designed for the determination of optimal routes. Initially, the dynamic cluster WSN is simulated using energy, mobility, trust, and Link Life Time (LLT) models. The deep neuro-fuzzy network (DNFN) is utilized for the prediction of residual energy of nodes and the cluster workloads are dynamically balanced by the dynamic clustering of data using a fuzzy system. The designed Flamingo Jellyfish Search Optimization (FJSO) model is used for tuning the weights of the fuzzy system by considering different fitness parameters. Moreover, routing is performed using FJSO model which is used for the identification of optimal path to transmit data. In addition, the experimentation is done using MATLAB tool and the results proved that the designed FJSO model attained maximum of 0.657J energy, a minimum of 0.739 m distance, 0.649 s delay, 0.849 trust, and 0.885 Mbps throughput.

Optimizing Disaster Communication Models for Trust and Participation

This study examines the disaster communication models employed by the Tabalong Regency BPBD in South Kalimantan, Indonesia, focusing on trust-building and community participation in flood disaster management. Employing a qualitative approach, the research delves into the use of social media platforms, particularly WhatsApp, Instagram, and Facebook, by disaster stakeholders, including BPBD and the Self-Help Disaster Management Unit (UPBS). Literature study and field observations, including direct observations and stakeholder interviews, contribute to data collection. Interactive Model Data Analysis is utilized for data analysis. The findings reveal that Tabalong's effective disaster communication relies on trustworthy information dissemination through social media. Dialogical communication, facilitated by UPBS, enhances community trust and active participation. The models are characterized by ongoing dialogue, feedback mechanisms, and community-driven initiatives. The study underscores the success of Tabalong's disaster communication models in building trust and fostering community involvement. Challenges in model transferability to other regions necessitate contextual adaptations, emphasizing the importance of local organizations and supportive social settings. The research contributes insights into enhancing disaster resilience through tailored communication strategies.

Role of shared identity and agency trust in online voting among Finnish citizens

This study examined the impact of shared identity and agency trust, governmental vs. third party, on Finnish citizens' intention to vote online. Using the integrated model of shared identity and trust as a theoretical lens, a within-subject quasi-experiment was conducted to understand the impact of agency trust on intention to vote online. The model was tested using data from 248 Finnish citizens using PLS-SEM. We found that citizens’ perceptions of shared identity with online voting agencies significantly contribute to agency trust. This trust in agencies, then directly and indirectly through perceived usefulness, affects online voting intention. Perceived usefulness directly and perceived ease of use indirectly increase the intention to vote online. However, the perceived usefulness of online voting is contingent upon the voting administering agency being the government. This study contributes to the understanding of agency trust in online voting adoption in the Finnish context and highlights the role of shared identity in building citizen trust in online voting. It also emphasizes the effect of voting agency type on the perceived usefulness of online voting.

TA-denseNet: Efficient hardware trust and assurance model based on feature extraction and comparison of SEM images and GDSII images

Hardware trust and assurance, which relies on extracting and analyzing information from GDSII images and SEM images of integrated circuits, plays a critical role in ensuring the integrity, privacy, security, and functionality of integrated circuits. However, with the continuous improvement of integrated circuits integration, traditional approaches for hardware trust and assurance face great challenges owing to the low efficiency, low precision, and high cost. To solve the issue mentioned above, we propose a novel, automatic, and efficient deep learning-based method, called TA-denseNet, which directly learns the mapping from GDSII images and SEM images to the compact Euclidean space, where the distances between GDSII images and SEM images directly correspond to measures of similarity. Also, a new hardware trust and assurance dataset for model training and evaluation, named TA-dataset, is proposed. Finally, we evaluate the accuracy of the proposed TA-denseNet model in similarity measures between GDSII images and SEM images on the test sets of the proposed dataset and obtain an accuracy of over 99.83%, which shows that the TA-denseNet model proposed in this paper has good performance on direct feature extraction and comparison between GDSII images and SEM images.

QIoTChain: Quantum IoT‐blockchain fusion for advanced data protection in Industry 4.0

AbstractThe modern world has its foundation built on data. Data is generated all the time, and many aspects of the society and economy are built upon it. In today's world, data is employed in better decision‐making, innovation, and improving the efficiency of systems. It becomes essential to protect it from being tampered with or misused. Encryptions based on cryptographic systems are used in mainstream data protection these days. There have been newer iterations of mechanisms based on new techniques using blockchain and the zero trust model and even systems that employ fog computing along with content delivery networks. The advent of computation through quantum technologies also helped in developing data security with post‐quantum mechanisms. The critical conditions for the working of these mechanisms hinder their adoption primarily. This also leads current systems to lack one or more of these technologies. Thus, the importance of a unified system that can employ quantum technologies effectively becomes prominent and requires a definitive investigation. In this article, a Quantum‐IoT‐based data protection scheme that can be of value to Industry 4.0 is analyzed. Light is also shed on innovations in quantum computing that can help solve not just current problems but also the future.

A survey on IoT trust model frameworks

Trust can be considered as a multidisciplinary concept, which is strongly related to the context and it falls in different fields such as Philosophy, Psychology or Computer Science. Trust is fundamental in every relationship, because without it, an entity will not interact with other entities. This aspect is very important especially in the Internet of Things (IoT), where many entities produced by different vendors and created for different purposes have to interact among them through the internet often under uncertainty. Trust can overcome this uncertainty, creating a strong basis to ease the process of interaction among these entities. We believe that considering trust in the IoT is fundamental, and in order to implement it in any IoT entity, it is fundamental to consider it through the whole System Development Life Cycle. In this paper, we propose an analysis of different works that consider trust for the IoT. We will focus especially on the analysis of frameworks that have been developed in order to include trust in the IoT. We will make a classification of them providing a set of parameters that we believe are fundamental in order to properly consider trust in the IoT. Thus, we will identify important aspects to be taken into consideration when developing frameworks that implement trust in the IoT, finding gaps and proposing possible solutions.

Internet Banking Fulfilment and Customer Trust: a Study of Bauchi State Tertiary Institutions

The utilization of the Internet propels information technology and significantly enhances e-service quality criteria fulfillment. Customers have indicated their discontent with the fulfillment of e-service quality and customer trust, despite the banking industry’s continued process advancements. Customers are hesitant to utilize Internet banking as a result. In this study, the E-S-QUAL model and the interpersonal trust model were used to examine the impact of Internet banking fulfilment on consumer trust. In order to gather data for this study using a quantitative research approach, a structured questionnaire was employed. Data analysis methods included SPSS and PLS-SEM. The outcome demonstrates that Internet banking fulfillment has a substantial influence on consumer trust. According to this study, customer trust is impacted by how well internet banking works. Because these services tend to complete transactions on time, it has been shown that online banking completion has a substantial influence on consumers’ confidence. It was recommended that banks make every effort to simplify and improve the use of their systems. Due to a language barrier, customers shouldn’t be unable to perform online transactions.

Trust Modelling and Verification Using Event-B

Trust Modelling and Verification Using Event-B

PUFGuard: Vehicle-to-Everything Authentication Protocol for Secure Multihop Mobile Communication

Vehicle area networks (VANs) encompass a spectrum of communication modes, including point-to-point visible light communication, 5G/6G cellular wireless communication, and Wi-Fi ad hoc multihop communication. The main focus of this paper is the introduction and application of physically unclonable functions (PUFs) as a pivotal element in secure key generation, authentication processes, and trust metric definition for neighboring vehicles. The multifaceted protocols proposed herein encompass comprehensive security considerations, ranging from authentication and anonymity to the imperative aspects of the proof of presence, freshness, and ephemeral session key exchanges. This paper provides a systematic and comprehensive framework for enhancing security in VANs, which is of paramount importance in the context of modern smart transportation systems. The contributions of this work are multifarious and can be summarized as follows: (1) Presenting an innovative and robust approach to secure key generation based on PUFs, ensuring the dynamic nature of the authentication. (2) Defining trust metrics reliant on PUFs to ascertain the authenticity and integrity of proximate vehicles. (3) Using the proposed framework to enable seamless transitions between different communication protocols, such as the migration from 5G/6G to Wi-Fi, by introducing the concept of multimodal authentication, which accommodates a wide spectrum of vehicle capabilities. Furthermore, upholding privacy through the encryption and concealment of PUF responses safeguards the identity of vehicles during communication.

To be a freshman during the COVID-19 pandemic: A cross-lagged model of depression, mentalizing, and epistemic trust.

Research has shown that severity of depression increased in freshmen during their first months at university due to increased social and academic pressures. Since the start of the COVID-19 pandemic, several cross-sectional studies have suggested that levels of depression in university students are higher than before the pandemic, but longitudinal data are largely lacking. This study investigated severity of depression and negative affect linked to the pandemic among freshmen during their first semester at a large university in Flanders, Belgium. We also investigated whether epistemic trust predicted severity of depression and pandemic-related negative affect and whether problems with reflective functioning (or mentalizing) mediated these relations. Participants in this two-wave prospective study were 289 first-year students of the Faculty of Psychology and Educational Sciences of a large Belgian university. We conducted paired samples t-tests and cross-panel analysis to answer the research question. The number of students at risk of clinical depression increased by 41% between T1 (early October 2020) and T2 (late December 2020). Epistemic mistrust at T1 was prospectively associated with an increase in the prevalence and severity of depression at T2. Problems with mentalizing and negative COVID-19-related affect were positively associated with severity of depression at T2 and mediated the association between epistemic mistrust and severity of depression at T2. The findings highlight the key role of epistemic trust in the development of depression among freshmen, with the COVID-19 pandemic presenting an additional source of uncertainty.

Identifying Trustful Node Using Hybrid Trust Evaluation Method

A wireless sensor network referred as (WSN) is defined as a collection of distributed sensor nodes to work together for monitoring the physical and environmental conditions. Trust metrics in the wireless sensor networks is denoted as the important problem and it helps in solving the problem of access control, reliable communication, privacy and secure routing scheme. This paper establishes a new kind of approach to evaluate the trust value in WSN. The proposed algorithm and its evaluation made on the trust value of each node seen in the network depends on the metrics, trust attributesand trust parameters.

A Trust and Node Capability Model for Reliable and Secure MANET Communication

The Mobile Ad-hoc Network (MANET) is a rapidly deployable network. That is valuable for industrial and domestic applications due to flexible, mobile, and wireless communication. But the network is constrained with resources and security. In this paper, we are presenting a node capability based trusted routing named TNC-AODV for MANET. It is a hybrid approach for maintaining route reliability and security. The model is composed of the property of node capability and Trust. The Node capability is defined by the quality of service parameters like remaining energy, available bandwidth, buffer length, and mobility pattern. The aim is to ensure the discovery of reliable routes. Additionally, the trust is implemented by using a local and global trust for securing the network. The TNC-AODV is implemented through modification of AODV routing. That routing technique has been tested on three security threats namely Black-hole, wormhole, and DOS flooding attack. The simulation has been carried out using the NS2 simulator. The experimental results demonstrate that TNC-AODV provides security against attacks. Additionally, improve the packet delivery ratio, and throughput. Finally, the possible and feasible future extension of the work has also been proposed.

Affective Approach for Routing Attack Detection for Ad- hoc Mobile Networks Based on Trust Model

Affective Approach for Routing Attack Detection for Ad- hoc Mobile Networks Based on Trust Model

Privacy-preserving trust management method based on blockchain for cross-domain industrial IoT

The Industrial Internet of Things (IIOT) contains many devices from different autonomous domains (e.g., factories), which need to cooperate to complete complex manufacturing process. Devices from different domains collaborate with each other, which greatly raises trust concerns about device-to-device interactions. Existing trust management approaches may result in the risk of privacy leakage and low accuracy of trust evaluation. Thus, trust issues during interaction remain unsolved but imperative. In this paper, we propose a blockchain-based privacy-preserving trust management architecture PPTMA. Specifically, PPTMA adopts federated learning to train a task-specific trust model for different collaborative task. Our work is the first attempt to research the relationship between the weight calculation of trust metric and the change of context in the field of trust management. To preserve the privacy of devices, differential privacy (DP) is exploited during the trust evaluation process. In addition, a game theory-based incentive mechanism is proposed to encourage the IIOT device for actively and honestly submitting the trust data into the blockchain as so to promote the accuracy of trust computing. Finally, we also design a parallel consensus protocol (OPBFT) which realizes an assembly line to speed up the efficiency of the consensus process. The idea of consensus assembly line firstly proposed by us brings new opportunities for improving the consensus efficiency. Extensive experiments have been conducted to show the effectiveness and efficiency of the proposed method.

Communication and Content Trust Aware Routing For Clustered IoT Network

Security has become a major concern in practical applications related to Internet of Things, a Trust Aware Routing is found as second line of defence. To ensure a secure and hassle-free communication in IoT, this paper proposes a new routing strategy called as Communication and Content Trust Aware Routing (CCTAR) for Clustered IoT network. CCTAR is applied on a clustered IoT network in which the entire nodes are clustered into different clusters. Distance, initial energy, transmission range, angle of overlap and the sensing range are the fur major metrics used to cluster the network into hierarchical clusters followed by Cluster Head Selection. Next, the Trust Aware routing computes three different trust metrics namely Nobility rust, bilateral trust and Data oriented trust to determine the trustworthiness of Cluster Heads. The experimental evaluation of the proposed mechanism shows its superiority in terms of malicious nodes identification, Storage overhead reduction and Network lifetime improvisation.

Multi-Institutional Analysis of Cancer Patient Exposure, Perceptions, and Trust in Information Sources Regarding Complementary and Alternative Medicine.

Complementary and alternative medicine (CAM) use during cancer treatment is controversial. We aim to evaluate contemporary CAM use, patient perceptions and attitudes, and trust in various sources of information regarding CAM. A multi-institutional questionnaire was distributed to patients receiving cancer treatment. Collected information included respondents' clinical and demographic characteristics, rates of CAM exposure/use, information sources regarding CAM, and trust in each information source. Comparisons between CAM users and nonusers were performed with chi-squared tests and one-way analysis of variance. Multivariable logistic regression models for trust in physician and nonphysician sources of information regarding CAM were evaluated. Among 749 respondents, the most common goals of CAM use were management of symptoms (42.2%) and treatment of cancer (30.4%). Most CAM users learned of CAM from nonphysician sources. Of CAM users, 27% reported not discussing CAM with their treating oncologists. Overall trust in physicians was high in both CAM users and nonusers. The only predictor of trust in physician sources of information was income >$100,000 in US dollars per year. Likelihood of trust in nonphysician sources of information was higher in females and lower in those with graduate degrees. A large proportion of patients with cancer are using CAM, some with the goal of treating their cancer. Although patients are primarily exposed to CAM through nonphysician sources of information, trust in physicians remains high. More research is needed to improve patient-clinician communication regarding CAM use.

DoSRT: A Denial-of-Service Resistant Trust Model for VANET

Abstract 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.

Building trust in a zero-trust world: Enhancing network security

This paper aims to introduce the concept of Zero Trust as a new-generation framework for protection against threats, comparing it with perimeter security measures. In the light of new and evolving complex threats, especially phishing, insider threats, and data theft, organizations have turned a new leaf. Users and devices are constantly verified in Zero Trust, which means the key concept of never trust; always verify in the system means severe threats and lateral moves in networks are minimized. The work also examines the successes of Zero Trust as the strategy, Google’s BeyondCorp, and Capital One’s evolution in response to a breach, stressing its utility in protecting data and increasing organizational performance. Nonetheless, it is essential to examine the drawbacks of the Zero Trust model closely – this model is based on complex technology, the implementation of which requires significant investments; many users may also resist the introduction of Zero Trust; besides, the integration of Zero Trust with currently operating systems may also pose certain problems. The study also suggests adopting a phased approach, focusing on people and culture, embracing human factors, and using micronized cloud services. Moving into the future, a continuous convergence with Artificial Intelligence, Cloud-Native Security, Security, and SASE reference architectures will be important in enhancing Zero Trust modularity and minimizing management complexities. When it comes to the application and making it more mainstream, handling legacy system integration will also be an issue to tackle. Thus, I have concluded that Zero Trust can offer a sound foundation for a proactive cybersecurity strategy that will hold up well in the challenging environment of the forthcoming years. However, the implementation of Zero Trust requires significant preparation and severe capital investment to address its difficulties.

An optimized multi-hop routing protocol for wireless sensor network using improved honey badger optimization algorithm for efficient and secure QoS

A wireless sensor network (WSN) is a significant technology that might contribute in the industrial revolution. Sensor Nodes (SNs) in WSNs are equipped with a battery as a power source. Energy is the most critical resource for WSNs since the battery cannot be replaced or refilled. Several solutions have been devised and implemented over the years to preserve WSNs, which are a scarce resource. This study suggested a model to solves the energy issue as well as security issue and presents an optimized multi-hop routing in WSNs based on improved Honey Badger Algorithm (I-HBA). This method for multi-hop routing involves two steps: selecting a Cluster Head (CH) and routing the data packets. The I-HBA is used to pick energy-efficient CHs for efficient data transfer. The SNs then send their data through the chosen CH, which forwards it to the base station (BS) using the most efficient number of hops. The suggested I-HBA is used to select the finest hops. The proposed model employs the multi-objective fitness function including eight parameters for effective routing in the WSNs. Furthermore, security conscious multihop routing is carried out by employing a trust model that includes direct and indirect trust, data, integrity and forwarding rate factor. Additionally, the proposed I-HBA based routing protocol compared with the existing protocol such as LEACH, HEED, and PSO; and the proposed protocol outperforms the existing models. The findings show that for the same number of iterations, the whole quantity of data packets receives by the WSN using the proposed I-HBA based multihop routing protocol is substantially larger than for the PSO, LEACH, and HEED-based models.