Verify and trust: A multidimensional survey of zero-trust security in the age of IoT

The zero-trust (ZT) model assumes that all users, devices, and network traffic should not considered as trusted until proven. The Zero-trust model emphasizes the importance of verifying and authenticating every user and device, and limiting access to resources based on the principle of least privilege. Under the principle of the zero-trust model, devices are granted access after they have been successfully presented with their authentication credentials and access rights based on different factors, such as user identity, device health, location, and behaviour. Access controls are then continuously evaluated and updated as user properties, locations and behaviour change. The zero-trust model can be applied in various domains (healthcare, manufacturing, financial services, government etc.) to provide a comprehensive approach to cybersecurity that helps organizations to reduce risk and protect critical assets. This paper aims to provide a comprehensive and in-depth analysis of the zero-trust model, its principles, and its applications, as well as to propose recommendations for organizations looking to adopt this approach. We explore the major components of the zero-trust framework and their integration across different practical domains. Finally, we provide insightful discussions on open research issues within the zero-trust model in terms of the security and privacy of users and devices. This paper should help researchers and practitioners understand the importance of a zero-trust framework and adopt the zero-trust model for effective security, privacy, and resilience of their networks

“Be a Pattern for the World”: The Development of a Dark Patterns Detection Tool to Prevent Online User Loss

Dark Patterns are designed to trick users into sharing more information or spending more money than they had intended to do, by configuring online interactions to confuse or add pressure to the users. They are highly varied in their form, and are therefore difficult to classify and detect. Therefore, this research is designed to develop a framework for the automated detection of potential instances of web-based dark patterns, and from there to develop a software tool that will provide a highly useful defensive tool that helps detect and highlight these patterns

Minding the Gap: Computing Ethics and the Political Economy of Big Tech

In 1988 Michael Mahoney wrote that “[w]hat is truly revolutionary about the computer will become clear only when computing acquires a proper history, one that ties it to other technologies and thus uncovers the precedents that make its innovations significant” (Mahoney, 1988). Today, over thirty years after this quote was written, we are living right in the middle of the information age and computing technology is constantly transforming modern living in revolutionary ways and in such a high degree that is giving rise to many ethical considerations, dilemmas, and social disruption. To explore the myriad of issues associated with the ethical challenges of computers using the lens of political economy it is important to explore the history and development of computer technology

Technical Debt is an Ethical Issue

We introduce the problem of technical debt, with particular focus on critical infrastructure, and put forward our view that this is a digital ethics issue. We propose that the software engineering process must adapt its current notion of technical debt – focusing on technical costs – to include the potential cost to society if the technical debt is not addressed, and the cost of analysing, modelling and understanding this ethical debt. Finally, we provide an overview of the development of educational material – based on a collection of technical debt case studies - in order to teach about technical debt and its ethical implication