Building a View

Amid growing concerns about security and privacy, and their impact on decision-making, researchers have sought to understand the reasons behind users’ seemingly risky behaviour in disregarding security advice. In this study, we delve into the perceptions of security experts on end users’ threat models and their cybersecurity practices and habits. This research explores the perceptions of security and privacy experts regarding end users’ threat models and their behaviours in relation to cybersecurity. A survey was conducted with 55 experts, including 27 females and 28 males, to gain insights into end users’ habits, practices, and feelings from the perspective of security experts. The study reveals that end-users express moderate concern about privacy and security while carrying out their daily tasks. However, security experts believe that end-users tend to be passive towards organisational security policies, and their lack of knowledge about these policies which may lead to negative feelings. Additionally, experts perceive that end-users may be unaware of security measures, have difficulties understanding security concepts, and are at high risk of falling victim to phishing attacks by opening attachments and clicking on unknown links.

Process mining techniques provide capabilities for discovering the real business process flows from data, and compare expected and actual behaviors. Actual behaviors, in many cases, are obtained from Enterprise Resource Planning (ERP) systems and other enterprise information systems transaction logs. These transaction logs provide valuable insight into the companies' business processes. They traditionally hold a large amount of data in a set of conceptual documents related to each other through one-to-many and many-to-many relations, where information changes occur in transactions. Underlying data model gives rise to complex interactions between multiple data objects without a clear notion of a unique case identifier in an isolated process. However, enterprise process mining techniques can be applied only to event logs containing event data related to one notion of process instances. Within ERP systems, such event logs are not explicitly given and substantial domain knowledge is required to select the right data from multiple tables in relational databases. In order to respond to this need, in this paper we present an abstract syntax of domain specific language (DSL) for facilitating the extraction of an appropriate dataset from ERP systems by domain experts, and its conversion into event log based on XES IEEE standard. It is developed specifically to describe behavior over complex data from ERP systems in terms of multiple interacting artifacts. The goal is to align the data and process perspectives, supporting extraction of complex ambiguous cases, affected by data convergence and data divergence problems. The basic concepts of the language as well as principles are discussed in depth in this paper.

The recommendation system plays very important role in the ecommerce domain to recommend the relevant products/services based on end user preference or interest. This helps end users to easily make the buying decision from a vast range of product and brands are available in the market. A lot of research is done in recommendation system, aim to provide the relevant product to the end user by referring end user past purchase history, transaction details etc. In our Multi scenario demographic hybrid (MDH) approach, important demographic influence factors like the user age group and located area are considered. The products are also ranked with associated age group category. The experimental results of the proposed recommendation system have proven that it is better than the existing systems in terms of prediction accuracy of relevant products.

We acquire or provide most of the services with the help of the Internet in the fast-growing world. We should deploy various kinds of systems globally so that other users can practice the same effortlessly, and use storage framework to provide conveniences world-wide effectively. Nowadays, we practice vehicular communication technology to exchange diversified data for varied intentions, which helps vehicle operators in diversified manners such as traffic awareness, weather conditions, road assistance, automatic toll payment system, etc. In this chapter, we converse about data transmission through vehicular ad-hoc networks, cloud computing in the vehicular technology. After that, we survey of different schemes related to secure routing and geo-location details of vehicles. We illustrate concerning security demands, possible attacks, and challenges in the vehicular cloud computing (VCC) architecture. Conclusively, we suggest a new identification scheme to get access of the VCC system from the user end, which can be secured against varied attacks. Moreover, we do analysis of the suggested system to determine security worthiness and measure total required time to execute the phases.

CYBERNEWS

Self-efficacy in information security: Its influence on end users' information security practice behavior

Many people are dependent on desktop end user tools such as spreadsheets and databases to manage their data. While they may have the technical skills to set up data repositories, many end users lack the analysis skills to design data models which reflect their often deceptively complex requirements. We advocate that a comprehensive data model should always be developed, with expert help, so that the end user can feel confident the subtleties of the data are fully understood. We then suggest that some pragmatic decisions can be made to simplify the model so that the end user can retain control over setting up and maintaining the application.

The legend used to map a given region is most of the time the first characteristic assessed by end users. In GEOBIA, the choice of a classification system is primarily guided by the scale of analysis and the underlying data model. This study focuses on the classification systems related to spatial regions. Depending on the purpose of the GEOBIA analysis, three types of classification systems have been identified: majority-based, LCCS like and feature-based. The validation of the classification results then relies on an appropriate choice of response design, which in turn influences the accuracy results. The impact of the different response designs is highlighted with a real case study. A high accuracy pixel-based classification of the segmented image is used as synthetic reference and classification errors are simulated on the object database. The results shows that all methods are equivalent if all polygons are pure, but that the uncertainty conferred by under-segmented or heterogeneous polygons affects the accuracy estimates in different ways: some methods tolerate delineation or under-segmentation while others are directly affected. This experiment also shows some differences and similarities between the point-based and the object-based validation frameworks, which are both valid depending on the circumstances but could be misleading when they are not applied with the right purpose. The thematic precision of the legend is also discussed: the LCCS authorize mixtures of different spatial entities in the class definition, but the information content differs depending of the purity of the majority of the classes.

End users are said to be "the weakest link" in information systems (IS) security management in the workplace. They often knowingly engage in certain insecure uses of IS and violate security policies without malicious intentions. Few studies, however, have examined end user motivation to engage in such behavior. To fill this research gap, in the present study we propose and test empirically a nonmalicious security violation (NMSV) model with data from a survey of end users at work. The results suggest that utilitarian outcomes (relative advantage for job performance, perceived security risk), normative outcomes (workgroup norms), and self-identity outcomes (perceived identity match) are key determinants of end user intentions to engage in NMSVs. In contrast, the influences of attitudes toward security policy and perceived sanctions are not significant. This study makes several significant contributions to research on security-related behavior by (1) highlighting the importance of job performance goals and security risk perceptions on shaping user attitudes, (2) demonstrating the effect of workgroup norms on both user attitudes and behavioral intentions, (3) introducing and testing the effect of perceived identity match on user attitudes and behavioral intentions, and (4) identifying nonlinear relationships between constructs. This study also informs security management practices on the importance of linking security and business objectives, obtaining user buy-in of security measures, and cultivating a culture of secure behavior at local workgroup levels in organizations.

The user interaction (UI) function is enhanced to the IMS service architecture by defining the data model between the UI client and the UI server, introducing the user behavior trace function (UBTF), enhancing the interface between a CSCF and a PDFE. Unlike traditional services such as Call Forwarding, Ring Back Tone, etc., which are based on an end user status and/or a pre-provisioned user profile, the services built on this extended architecture enables end users to actively and instantly intervene in session control. The user preferences will be statistically collected into the network by the UBTF based on historical user behavior, and used for future sessions. And the UBTF can also improve the user experience by changing the UI function of a service and cut down the network resource usage.

IP multicast is increasingly seen as efficient mode of live content distribution in the Internet to significantly large subscriber bases. Despite its numerous benefits over IP unicast, multicast has not seen widespread deployment over modern networks. Network complexity and session discovery issues have plagued IP multicast since its inception. The Internet research community is in general agreement to move over to SSM (Source Specific Multicast).With IGMP v 3 (Internet Group Management Protocol) and SSM, the source discovery burden will rest with the end user. Channel discovery is one of the few stumbling blocks remaining to be solved for successful and widespread deployment of multicast. In an earlier work a DNS (Domain Name System) aware multicast session discovery architecture, mDNS, has been proposed which is distributed, hierarchical and globally scalable.This paper proposes to leverage the mDNS architecture by enabling multicast sessions to be tagged using geographical and spatial information based on the channel contents or service provider location. It further proposes automatic geo-coding of session registration information as the content provider registers session information with mDNS. It also provides necessary design changes and gives data models and data structures to support seamless location sensitive session retrieval as part of search query results to be furnished to the end user. The paper includes envisaged scenarios in which geo-tagging would enhance end user experience and would enable smarter query result generation.

The goal of the National Cancer Institute’s cancer Biomedical Informatics Grid initiative, or caBIG®, is the ability to share data and resources among cancer researchers. One means to achieving this goal is the development of semantically interoperable informatics tools based on common data models and controlled vocabularies. A tool for managing biospecimen repositories, caTissue, enables investigators to query for available tissues that are relevant to the needs of their research. For this functionality, the caTissue application data model must include annotation describing various specimen characteristics, and have this information accessible for query by the researcher end user. Having deployed caTissue over two years at Thomas Jefferson University, we report the lessons learned from our investigators’ use of this complex, semantically interoperable software application. Overall we have found that object model complexity and semantic completeness pose obstacles to end user accessibility that require effective strategies to overcome.

Due to the recent pandemic, video conferencing platforms – once niche products aimed at limited communities have become a pervasive way of conducting business and sustaining social connections on a global scale. This project explored cybersecurity vulnerabilities and risks faced by these platforms – their data, hardware, and the information exchanged during virtual meetings – and explains some ways these issues can be mitigated. Published research was compiled and analyzed to uncover general risks, vulnerabilities, and security measures. Then, three popular platforms – Zoom, Skype and GoToMeeting were subjected to closer scrutiny. Findings show that platform vendors, business organizations, education institutions, and end users all bear responsibility to train themselves and their constituents on specific cybersecurity steps to enhance video conferencing security. Targeted recommendations are shared, along with some opportunities to build upon this research in the future.

ObjectivesPatient-reported outcomes (PROs) are increasingly used to measure the patient’s perspective of their outcomes following healthcare interventions. The aim of this study was to determine the preferred formats for reporting...

As machine learning (ML) models are used more and more for important business tasks, it is important to make sure that ML operations are safe. This makes it easier to automate tasks, make decisions, and come up with fresh ideas in a number of fields. These procedures generally comprise sensitive datasets, proprietary algorithms, and complex pipelines that go through many phases, such as taking in data, preparing it, training it, validating it, and deploying it. These pipelines are open to data corruption, model changes, and illegal access since they don't have strong security measures in place. All of these things might make the models substantially less accurate and reliable. Kubernetes is the best platform for building and scaling machine learning operations because it has strong container orchestration features, declarative configuration, and works with the latest MLOps tools. It is always changing and falling apart, however, which means it has its own security problems that need to be dealt with carefully and with careful preparation. This article talks about how to use Kubernetes and follow the best practices and principles of the Certified Kubernetes Security Specialist (CKS) framework. This post's purpose is to provide you knowledge on how to make machine learning pipelines safer. We will talk about how to set up strong Role-Based Access Control (RBAC) based on the principle of least privilege, how to create detailed network policies to keep workloads separate, how to safely manage secrets using both native Kubernetes tools and third-party tools, and how to use runtime security measures like container image scanning and admission controllers to make sure that everything stays safe at all times. In the context of building machine learning systems that can solve problems, we want to stress how important DevSecOps concepts are. This list includes things like putting security first, automating the process of finding vulnerabilities, and always keeping an eye on pipelines. In this case, the security rules that come with Kubernetes are applied. Companies may build machine learning systems that are scalable, auditable, and safe utilizing methods that CKS has approved. This lowers risks and encourages new ideas. There is a case study at the end of the post that shows how a tiered security policy may make a real-world machine learning pipeline better. This is solid advice for anybody who is working with Kubernetes-based machine learning systems when it comes to putting security first