An Integrity Measurement Scheme for Containerized Virtual Network Function

The deployment of virtual network function (VNF) in the container can realize the 5G service-based architecture (SBA) with high flexibility. The container carrying the VNF has poor isolation and low protection capabilities, and there is a security risk of being tampered and replaced. Current security protection technologies such as access control, intrusion detection, and virus detection cannot ensure that the container is not illegally modified. In order to fundamentally protect the integrity of containerized VNFs, this paper proposes a containerized VNF trust measurement scheme container integrity measurement (CIM). The scheme extends the chain of trust to bare metal containers and virtual machine containers, and experiments are carried out in a containerized VNF communication environment. The results show that the integrity measurement protection scheme is effective. Compared with ordinary containers, the average CPU usage of trusted containers has increased by 26%, and the average memory usage growth rate is less than 1%, the performance overhead caused by CIM is acceptable.

Can we trust measures of trust? a comparison of results from open and closed questions

Many public opinion surveys compare trust in a number of different information and (mediated) knowledge sources, typically using closed questions with a set of answer categories that are imposed by researchers. We aim to validate these categories by quantitatively comparing survey responses about trustworthy sources using open and closed questions, and by qualitatively analyzing the open answers. The results show that answer options typically used for closed questions in academic research are generally valid and closely match categories that respondents come up with unprimed. In some cases, answers to open questions can be non-exhaustive, particularly when sources are considered trustworthy but are not salient for respondents. Open questions, however, may still be useful for exploratory research or more detailed investigations of media diets on the outlet-level. Qualitative approaches to open questions can also give more insight into motivations for distrust, e.g. perceptions of inconsistency or a fundamental rejection of the shared factual basis of an issue. In addition, our results indicate that respondents’ interpretation of answer categories may change reported levels of trust: those that think of more specific outlets tend to report higher general media trust. This study provides new insights into how question design, and particularly the choice of answer options, may influence reported levels and sources of trust, and how qualitative and quantitative approaches to trust measurement can be combined.

Heterogeneous identity trust management method based on risk assessment

The cross-trust domain environment in which heterogeneous identity alliances are located often does not have a completely trusted centralized trust root, and different trust domains and entities also have specific security requirements. In view of the above problems, we believe that trust measurement of cross-domain identities based on risk assessment is an effective method to achieve decentralized proof of user identities in heterogeneous cyberspace. There are various risk assessment models. We choose the more mature attack graph theory in the existing research to apply to the new field of cross-trust domain management of heterogeneous identities. We propose an attribute attack graph evaluation model to evaluate cross-domain identities through risk measurement of attributes. In addition, heterogeneous identity alliances also have architectural risks, especially the risk of decentralized underlying structures. In response to this problem, we identify the risk of the identity alliance infrastructure, and combine the risk assessment and presentation system design to verify the principle.

Construction of Internet of things trusted group based on multidimensional attribute trust model

Accurate prediction of the trust relationship is the basis for trusted access and secure interaction between Internet of things nodes. To evaluate the degree of trust, a trust metric is assigned to every node depending on its several attributes. Normal nodes in Internet of things tend to suffer collusion attacks from malicious nodes; thus, the accuracy of the trust measurement decreases. To enhance the security of interaction between massive Internet of things nodes, we propose a multidimensional attribute trust model and a dynamic maintenance mechanism of a trusted group. The proposed model provides a reference for the selection and evaluation of node multidimensional attribute factors to adapt to different Internet of things application scenarios. The dispersion of satisfaction records is used to discover abnormal data and weaken its influence on the calculation of the node’s comprehensive trust evaluation. The construction of trusted groups provides an architectural foundation for the application of group signature that maintains low network overhead. The performance of multidimensional attribute trust model and dynamic maintenance mechanism is verified using Netlogo. Simulation results show the efficiency of the proposed model to classify the malicious nodes and honest nodes, as well as to build a trusted group that could ensure honest nodes occupy the major proportion.

Trust Measurement in Human–Automation Interaction: A Systematic Review

This systematic review summarizes current measurements of trust in human-automation interaction. A total of 217 articles were found, and it was determined that 44 articles contained relevant information and met inclusion criteria. The results of the review showed that 75% ( n = 33) of articles used subjective measures of trust only, and 41% ( n = 18) used researcher-defined methods of measuring trust instead of peer-reviewed and validated scales. Of 10 defined industries, the highest number of articles ( n = 14) were assigned to the automotive industry, followed by aviation, military, and security ( n = 6). The automated systems studied in relevant articles were decision aids, automated control and navigation systems, and process control systems. This review showed that research of trust in human-automation interaction (1) has the tendency to use subjective measures of trust as the primary or only measure, (2) has the tendency to individually define trust and how it is measured, and (3) is heavily composed of research on automotive automation. Best practices and future research are discussed.

A Factored Similarity Model with Trust and Social Influence for Top-N Recommendation

Many trust-aware recommendation systems have emerged to overcome the problem of data sparsity, which bottlenecks the performance of traditional Collaborative Filtering (CF) recommendation algorithms. However, these systems most rely on the binary social network information, failing to consider the variety of trust values between users. To make up for the defect, this paper designs a novel Top-N recommendation model based on trust and social influence, in which the most influential users are determined by the Improved Structural Holes (ISH) method. Specifically, the features in Matrix Factorization (MF) were configured by deep learning rather than random initialization, which has a negative impact on prediction of item rating. In addition, a trust measurement model was created to quantify the strength of implicit trust. The experimental result shows that our approach can solve the adverse impacts of data sparsity and enhance the recommendation accuracy.