Tackling the barriers to achieving Information Assurance

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.This original, reflective practitioner study researched whether professionalising IA could be successfully achieved, in line with the UK Cyber Security Strategy expectations. The context was an observed changing dominant narrative from IA to cybersecurity. The research provides a dialectical relationship with the past to improve IA understanding. The Academic contribution: Using archival and survey data, the research traced the origins of the term IA and its practitioner usage, in the context of the increasing use of the neologism of cybersecurity, contributing to knowledge through historical research. Discourse analysis of predominantly UK government reports, policy direction, legislative and regulatory changes, reviewing texts to explore the functions served by specific constructions, mainly Information Security (Infosec) vs IA. The Researcher studied how accounts were linguistically constructed in terms of the descriptive, referential and rhetorical language used, and the function that serves. The results were captured in a chronological review of IA ontology. The Practitioner contribution: Through an initial Participatory Action Research (PAR) public sector case study, the researcher sought to make sense of how the IA profession operates and how it was maturing. Data collection from self-professed IA practitioners provided empirical evidence. The researcher undertook evolutionary work analysing survey responses and developed theories from the analysis to answer the research questions. The researcher observed a need to implement a unified approach to Information Governance (IG) on a large organisation-wide scale. Using a constructivist grounded theory the researcher developed a new theoretical framework - i3GRC™ (Integrated and Informed Information Governance, Risk, and Compliance) - based on what people actually say and do within the IA profession. i3GRC™ supports the required Information Protection (IP) through maturation from IA to holistic IG. Again, using PAR, the theoretical framework was tested through a private sector case study, the resultant experience strengthening the bridge between academia and practitioners

Trusted computing or trust in computing? Legislating for trust networks

This thesis aims to address several issues emerging in the new digital world. Using Trusted Computing as the paradigmatic example of regulation though code that tries to address the cyber security problem that occurs, where the freedom of the user to reconfigure her machine is restricted in exchange for greater, yet not perfect, security. Trusted Computing is a technology that while it aims to protect the user, and the integrity of her machine and her privacy against third party users, it discloses more of her information to trusted third parties, exposing her to security risks in case of compromising occurring to that third party. It also intends to create a decentralized, bottom up solution to security where security follows along the arcs of an emergent “network of trust”, and if that was viable, to achieve a form of code based regulation. Through the analysis attempted in this thesis, we laid the groundwork for a refined assessment, considering the problems that Trusted Computing Initiative (TCI) faces and that are based in the intentional, systematic but sometimes misunderstood and miscommunicated difference (which as we reveal results directly in certain design choices for TC) between the conception of trust in informatics (“techno-trust”) and the common sociological concept of it. To reap the benefits of TCI and create the dynamic “network of trust”, we need the sociological concept of trust sharing the fundamental characteristics of transitivity and holism which are absent from techno-trust. This gives rise to our next visited problems which are: if TC shifts the power from the customer to the TC provider, who takes on roles previously reserved for the nation state, then how in a democratic state can users trust those that make the rules? The answer lies partly in constitutional and human rights law and we drill into those functions of TC that makes the TCI provider comparable to state-like and ask what minimal legal guarantees need to be in place to accept, trustingly, this shift of power. Secondly, traditional liberal contract law reduces complex social relations to binary exchange relations, which are not transitive and disrupt rather than create networks. Contract law, as we argue, plays a central role for the way in which the TC provider interacts with his customers and this thesis contributes in speculating of a contract law that does not result in atomism, rather “brings in” potentially affected third parties and results in holistic networks. In the same vein, this thesis looks mainly at specific ways in which law can correct or redefine the implicit and democratically not validated shift of power from customer to TC providers while enhancing the social environment and its social trust within which TC must operate

Next Generation Internet of Things – Distributed Intelligence at the Edge and Human-Machine Interactions

This book provides an overview of the next generation Internet of Things (IoT), ranging from research, innovation, development priorities, to enabling technologies in a global context. It is intended as a standalone in a series covering the activities of the Internet of Things European Research Cluster (IERC), including research, technological innovation, validation, and deployment.The following chapters build on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT–EPI), the IoT European Large-Scale Pilots Programme and the IoT European Security and Privacy Projects, presenting global views and state-of-the-art results regarding the next generation of IoT research, innovation, development, and deployment.The IoT and Industrial Internet of Things (IIoT) are evolving towards the next generation of Tactile IoT/IIoT, bringing together hyperconnectivity (5G and beyond), edge computing, Distributed Ledger Technologies (DLTs), virtual/ andaugmented reality (VR/AR), and artificial intelligence (AI) transformation.Following the wider adoption of consumer IoT, the next generation of IoT/IIoT innovation for business is driven by industries, addressing interoperability issues and providing new end-to-end security solutions to face continuous treats.The advances of AI technology in vision, speech recognition, natural language processing and dialog are enabling the development of end-to-end intelligent systems encapsulating multiple technologies, delivering services in real-time using limited resources. These developments are focusing on designing and delivering embedded and hierarchical AI solutions in IoT/IIoT, edge computing, using distributed architectures, DLTs platforms and distributed end-to-end security, which provide real-time decisions using less data and computational resources, while accessing each type of resource in a way that enhances the accuracy and performance of models in the various IoT/IIoT applications.The convergence and combination of IoT, AI and other related technologies to derive insights, decisions and revenue from sensor data provide new business models and sources of monetization. Meanwhile, scalable, IoT-enabled applications have become part of larger business objectives, enabling digital transformation with a focus on new services and applications.Serving the next generation of Tactile IoT/IIoT real-time use cases over 5G and Network Slicing technology is essential for consumer and industrial applications and support reducing operational costs, increasing efficiency and leveraging additional capabilities for real-time autonomous systems.New IoT distributed architectures, combined with system-level architectures for edge/fog computing, are evolving IoT platforms, including AI and DLTs, with embedded intelligence into the hyperconnectivity infrastructure.The next generation of IoT/IIoT technologies are highly transformational, enabling innovation at scale, and autonomous decision-making in various application domains such as healthcare, smart homes, smart buildings, smart cities, energy, agriculture, transportation and autonomous vehicles, the military, logistics and supply chain, retail and wholesale, manufacturing, mining and oil and gas

Unmet goals of tracking: within-track heterogeneity of students' expectations for

Educational systems are often characterized by some form(s) of ability grouping, like tracking. Although substantial variation in the implementation of these practices exists, it is always the aim to improve teaching efficiency by creating homogeneous groups of students in terms of capabilities and performances as well as expected pathways. If students’ expected pathways (university, graduate school, or working) are in line with the goals of tracking, one might presume that these expectations are rather homogeneous within tracks and heterogeneous between tracks. In Flanders (the northern region of Belgium), the educational system consists of four tracks. Many students start out in the most prestigious, academic track. If they fail to gain the necessary credentials, they move to the less esteemed technical and vocational tracks. Therefore, the educational system has been called a 'cascade system'. We presume that this cascade system creates homogeneous expectations in the academic track, though heterogeneous expectations in the technical and vocational tracks. We use data from the International Study of City Youth (ISCY), gathered during the 2013-2014 school year from 2354 pupils of the tenth grade across 30 secondary schools in the city of Ghent, Flanders. Preliminary results suggest that the technical and vocational tracks show more heterogeneity in student’s expectations than the academic track. If tracking does not fulfill the desired goals in some tracks, tracking practices should be questioned as tracking occurs along social and ethnic lines, causing social inequality

Next Generation Internet of Things – Distributed Intelligence at the Edge and Human-Machine Interactions

This book provides an overview of the next generation Internet of Things (IoT), ranging from research, innovation, development priorities, to enabling technologies in a global context. It is intended as a standalone in a series covering the activities of the Internet of Things European Research Cluster (IERC), including research, technological innovation, validation, and deployment.The following chapters build on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT–EPI), the IoT European Large-Scale Pilots Programme and the IoT European Security and Privacy Projects, presenting global views and state-of-the-art results regarding the next generation of IoT research, innovation, development, and deployment.The IoT and Industrial Internet of Things (IIoT) are evolving towards the next generation of Tactile IoT/IIoT, bringing together hyperconnectivity (5G and beyond), edge computing, Distributed Ledger Technologies (DLTs), virtual/ andaugmented reality (VR/AR), and artificial intelligence (AI) transformation.Following the wider adoption of consumer IoT, the next generation of IoT/IIoT innovation for business is driven by industries, addressing interoperability issues and providing new end-to-end security solutions to face continuous treats.The advances of AI technology in vision, speech recognition, natural language processing and dialog are enabling the development of end-to-end intelligent systems encapsulating multiple technologies, delivering services in real-time using limited resources. These developments are focusing on designing and delivering embedded and hierarchical AI solutions in IoT/IIoT, edge computing, using distributed architectures, DLTs platforms and distributed end-to-end security, which provide real-time decisions using less data and computational resources, while accessing each type of resource in a way that enhances the accuracy and performance of models in the various IoT/IIoT applications.The convergence and combination of IoT, AI and other related technologies to derive insights, decisions and revenue from sensor data provide new business models and sources of monetization. Meanwhile, scalable, IoT-enabled applications have become part of larger business objectives, enabling digital transformation with a focus on new services and applications.Serving the next generation of Tactile IoT/IIoT real-time use cases over 5G and Network Slicing technology is essential for consumer and industrial applications and support reducing operational costs, increasing efficiency and leveraging additional capabilities for real-time autonomous systems.New IoT distributed architectures, combined with system-level architectures for edge/fog computing, are evolving IoT platforms, including AI and DLTs, with embedded intelligence into the hyperconnectivity infrastructure.The next generation of IoT/IIoT technologies are highly transformational, enabling innovation at scale, and autonomous decision-making in various application domains such as healthcare, smart homes, smart buildings, smart cities, energy, agriculture, transportation and autonomous vehicles, the military, logistics and supply chain, retail and wholesale, manufacturing, mining and oil and gas