Enhanced dynamic source routing for verifying trust in mobile ad hoc network for secure routing

Secure data transfer in mobile ad hoc network (MANET) against malicious attacks is of immense importance. In this paper, we propose a new enhanced trust model for securing the MANET using trust-based scheme that uses both blind trust and referential trust. In order to do this, the trust relationship function has to be integrated with the dynamic source routing (DSR) protocol for making the protocol more secure. We thoroughly analyze the DSR protocol and generate the performance matrices for the data pertaining to packets sent, packets received, packets loss, and throughput. We also analyze the outcome attained from the improvised trust establishment scheme by using the three algorithm implementations in NS2 simulator for detecting and preventing various types of attacks.

An Adaptive Trust Model Based on Fuzzy Logic

In cooperative environments is common that agents delegate tasks to each other to achieve their goals since an agent may not have the capabilities or resources to achieve its objectives alone. However, to select good partners, the agent needs to deal with information about the abilities, experience, and goals of their partners. In this situation, the lack or inaccuracy of information may affect the agent's judgment about a given partner; and hence, increases the risk to rely on an untrustworthy agent. Therefore, in this work, we present a trust model that combines different pieces of information, such as social image, reputation, and references to produce more precise information about the characteristics and abilities of agents. An important aspect of our trust model is that it can be easily configured to deal with different evaluation criteria. For instance, as presented in our experiments, the agents are able to select their partners by availability instead of the expertise level. Besides, the model allows the agents to decide when their own opinions about a partner are more relevant than the opinions received from third parties, and vice-versa. Such flexibility can be explored in dynamic scenarios, where the environment and the behavior of the agents might change constantly.

Improving the Sustainable Usage Intention of Mobile Payments: Extended Unified Theory of Acceptance and Use of Technology Model Combined With the Information System Success Model and Initial Trust Model

Under the background of global cross-border mobile commerce (m-commerce) integration, the importance of cross-border payment research is becoming increasingly prominent and urgent. The important value of this study is to empirically research the influence power of key elements in using two different mobile payment (m-payment) platforms in Korea. The extended unified theory of acceptance and use of technology (UTAUT2) has been widely applied in various studies because of its strong interpretive power. In Korea, there are a few empirical studies on Chinese users. Based on a survey of 908 Chinese participants (486 WeChat Pay’s Chinese users and 465 Kakao Pay’s Korean users) in Korea, this study is one application extending UTAUT2 by incorporating multi-group and multi-model constructs: UTAUT2, information system success (ISS) model, and an initial trust model (ITM), considering a multi-group analysis with some mediating variables (payment difference). By comparing the two different payment platforms’ characters, this manuscript provides a set of targeted measures to ensure Chinese WeChat Payment platform decision-makers create effective long-term strategic policies for cross-border m-payments in Korea, and eventually, benefit cross-border m-commerce and economic cooperation in Southeast Asia.

Public E-participation Services as a Cure for Declining Voter Turnout - Acceptance Model Research Using PLS-SEM

Purpose of this paper is to identify factors influencing the intention to use and develop a model for measuring the intention to use public e-participation services. As a added value, paper is examining the structure of needs for different levels of public e-participation services. As for the methodology, this paper provides an empirical evaluation of Davis's Technology Acceptance Model extended with non-technical constructs of the Planned Behavior Theory and Trust Model. Validity and hypotheses of the newly proposed multidimensional structural model were tested using Partial Least Squares Structural Equation Modeling. PLS-SEM research results significantly confirmed three out of seven hypotheses. There is a positive and statistically significant correlation between “Expected usefulness”, “Expected behaviour control” and “Trust in the Internet” with the intention to use public e-participation services (p<0.05). Concerning demand-side, research results demonstrate that the majority of the respondents prefers public e-participation services of a higher level of complexity.

An optimized deep learning-based trust mechanism In VANET for selfish node detection

Purpose This study aims to develop a trust mechanism in a Vehicular ad hoc Network (VANET) based on an optimized deep learning for selfish node detection. Design/methodology/approach The authors built a deep learning-based optimized trust mechanism that removes malicious content generated by selfish VANET nodes. This deep learning-based optimized trust framework is the combination of the Deep Belief Network-based Red Fox Optimization algorithm. A novel deep learning-based optimized model is developed to identify the type of vehicle in the non-line of sight (nLoS) condition. This authentication scheme satisfies both the security and privacy goals of the VANET environment. The message authenticity and integrity are verified using the vehicle location to determine the trust level. The location is verified via distance and time. It identifies whether the sender is in its actual location based on the time and distance. Findings A deep learning-based optimized Trust model is used to detect the obstacles that are present in both the line of sight and nLoS conditions to reduce the accident rate. While compared to the previous methods, the experimental results outperform better prediction results in terms of accuracy, precision, recall, computational cost and communication overhead. Practical implications The experiments are conducted using the Network Simulator Version 2 simulator and evaluated using different performance metrics including computational cost, accuracy, precision, recall and communication overhead with simple attack and opinion tampering attack. However, the proposed method provided better prediction results in terms of computational cost, accuracy, precision, recall, and communication overhead than other existing methods, such as K-nearest neighbor and Artificial Neural Network. Hence, the proposed method highly against the simple attack and opinion tampering attacks. Originality/value This paper proposed a deep learning-based optimized Trust framework for trust prediction in VANET. A deep learning-based optimized Trust model is used to evaluate both event message senders and event message integrity and accuracy.

Trust Evaluation Model for Internet of Things using Mobile Ad hoc Networks

Internet of Things (IoT) is a theory that embraces various objects and methods of communication to exchange information. Today, IoT is more a descriptive term of a vision that everything should be linked to the internet. As many as the advantages of IoT, its uniqueness poses a new problem in establishing a trustworthy environment amid some of the items due to the lack of appropriate implementation machineries. However, some common network security measures are not adequate to preserve the integrity of information and services exchanged over the internet. As a result, they remain vulnerable to threats ranging from cyber-physical layer and social layer data management risks. IoT in smart environments interrelates with mobile ad hoc network (MANET), becoming even more attractive and economically successful. This research proposes two different metrics approaches namely Social trust metrics and Quality of Service to evaluate the trustworthiness of IoT Applications using MANET nodes and designing trust metrics that are computed using multiple properties of trust and quality of service. A model would be designed and simulated in NS-2. The findings of the investigation into the evaluation of trustworthiness and network performance are presented. The proposed system model has 99.31% accuracy when there were 10%malicious nodes in the network. When the number of misbehaving nodes was increased to 50%, the accuracy level dropped to 98.75 percent, a difference of only 0.56%. The results show that the security and trustworthiness of the proposed system is an improvement over the existing models like Collaborative Reputation (CORE) trust model and Ant Based Evidence Distribution (trust) model. The proposed model can be used to address security challenges in a network environment. Keywords— Confidence, Honesty, IoT, MANET, QoS, Trustworthiness.

Yes, science dial is extremely costly, but...

Those who believe in the simplistic trust model between politics and science unintentionally (or intentionally) omit the fact that there exists something called cultural value systems and norms, which govern the formation, growth and demise of a group. These elements are so critical that by setting them aside, we risk entering total disagreements whenever difficult problems arise. One such serious problem is the climate crisis and the need for building the eleventh cultural value as proposed by Vuong [7]. And this value will complement the progressive value system suggested by Harrison [8]. It is safe to say that besides hard-core sciences, resolving global problems posing existential threats to humankind will certainly require us to deploy our best weapons, and many must come from the social sciences and humanities [9]. Therefore, the future solution to global change problems will have to show us its social heart.

A Probabilistic Trust Model for Cloud Services Using Bayesian Networks

With the rapid adoption of cloud computing in the industry, there has been a significant challenge in managing trust between cloud service providers and service consumers. 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 trust or distrust of a service by the applicant is the different quality of services related to the service. Therefore, approaches are needed to assess the trustworthiness of cloud services with respect to the values ​​of 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 Bayesian networks. Bayesian network is a probabilistic graphical model that can be used as one of the best methods to control uncertainty. Using Bayesian network makes it possible to infer more accurate QoS values ​​will which leads to the selection of highly trustworthy services by several cloud service requesters. The results of the experiments show that the proposed trust model is highly accurate and significantly reduces the estimation error.

Factors Influencing Intent to Adopt Big Data Analytics in Malaysian Government Agencies

In Big Data Analytics (BDA), many government agencies directly raised their ICT expenditure in their effort to understand the attitude of the users towards new technologies. This research is intended to analyze factors affecting IT practitioners’ behavioral intentions in adopting (BDA) using a combination of multiple technology acceptance models. The synergistic three IS theory strengths: (1) Task Technology Fit (TTF), (2) Unified Technology Acceptance and Utilization Theory (UTAUT), and the (3) Initial Trust Model (ITM). The concept was validated in Malaysian government agencies, one of the highly dependent BDA promoters and initiators. 186 respondents in the Information Management departments of public agencies were recruited as part of the rigorous methodology to gather rich data. Partial least squares were analyzed by the structural models (PLS). The two key factors determine behavioral intention to adopt BDA in government agencies. Firstly, the assumption that the technology is going to produce great results raises the expectation of performance. Technological fit was the second determinant factor. Initial trust, on the other hand, was found to be adversely related to the BDA intention. Implicitly, the proposed model would be useful to IT officers in public agencies in making investment choices and designing non-adopter-friendly outreach strategies because they have more barriers to acceptance than adopters and lead adopters in the reward ladder. All public agencies will benefit from the findings of this study in gaining awareness of BDA application and fostering psychological empowerment of employees to adopt this revolutionary approach. The article outlines how dynamic TTF, UTAUT and ITM are for researchers to integrate in their emerging decision support framework for the study of new technology adoption.