Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical

IAMS framework: a new framework for acceptable user experiences for integrating physical and virtual identity access management systems

The modern world is populated with so many virtual and physical Identity Access Management Systems (IAMSs) that individuals are required to maintain numerous passwords and login credentials. The tedious task of remembering multiple login credentials can be minimised through the utilisation of an innovative approach of single sign-in mechanisms. During recent times, several systems have been developed to provide physical and virtual identity management systems; however, most have not been very successful. Many of the available systems do not provide the feature of virtual access on mobile devices via the internet; this proves to be a limiting factor in the usage of the systems. Physical spaces, such as offices and government entities, are also favourable places for the deployment of interoperable physical and virtual identity management systems, although this area has only been explored to a minimal level. Alongside increasing the level of awareness for the need to deploy interoperable physical and virtual identity management systems, this paper addresses the immediate need to establish clear standards and guidelines for successful integration of the two medium

Complex history of the amphibian-killing chytrid fungus revealed with genome resequencing data

Understanding the evolutionary history of microbial pathogens is critical for mitigating the impacts of emerging infectious diseases on economically and ecologically important host species. We used a genome resequencing approach to resolve the evolutionary history of an important microbial pathogen, the chytrid Batrachochytrium dendrobatidis (Bd), which has been implicated in amphibian declines worldwide. We sequenced the genomes of 29 isolates of Bd from around the world, with an emphasis on North, Central, and South America because of the devastating effect that Bd has had on amphibian populations in the New World. We found a substantial amount of evolutionary complexity in Bd with deep phylogenetic diversity that predates observed global amphibian declines. By investigating the entire genome, we found that even the most recently evolved Bd clade (termed the global panzootic lineage) contained more genetic variation than previously reported. We also found dramatic differences among isolates and among genomic regions in chromosomal copy number and patterns of heterozygosity, suggesting complex and heterogeneous genome dynamics. Finally, we report evidence for selection acting on the Bd genome, supporting the hypothesis that protease genes are important in evolutionary transitions in this group. Bd is considered an emerging pathogen because of its recent effects on amphibians, but our data indicate that it has a complex evolutionary history that predates recent disease outbreaks. Therefore, it is important to consider the contemporary effects of Bd in a broader evolutionary context and identify specific mechanisms that may have led to shifts in virulence in this system.Instituto de Botánica "Dr. Carlos Spegazzini

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical

Event and Intrusion Detection Systems for Cyber-Physical Power Systems

High speed data from Wide Area Measurement Systems (WAMS) with Phasor Measurement Units (PMU) enables real and non-real time monitoring and control of power systems. The information and communication infrastructure used in WAMS efficiently transports information but introduces cyber security vulnerabilities. Adversaries may exploit such vulnerabilities to create cyber-attacks against the electric power grid. Control centers need to be updated to be resilient not only to well-known power system contingencies but also to cyber-attacks. Therefore, a combined event and intrusion detection systems (EIDS) is required that can provide precise classification for optimal response. This dissertation describes a WAMS cyber-physical power system test bed that was developed to generate datasets and perform cyber-physical power system research related to cyber-physical system vulnerabilities, cyber-attack impact studies, and machine learning algorithms for EIDS. The test bed integrates WAMS components with a Real Time Digital Simulator (RTDS) with hardware in the loop (HIL) and includes various sized power systems with a wide variety of implemented power system and cyber-attack scenarios. This work developed a novel data processing and compression method to address the WAMS big data problem. The State Tracking and Extraction Method (STEM) tracks system states from measurements and creates a compressed sequence of states for each observed scenario. Experiments showed STEM reduces data size significantly without losing key event information in the dataset that is useful to train EIDS and classify events. Two EIDS are proposed and evaluated in this dissertation. Non-Nested Generalized Exemplars (NNGE) is a rule based classifier that creates rules in the form of hyperrectangles to classify events. NNGE uses rule generalization to create a model that has high accuracy and fast classification time. Hoeffding adaptive trees (HAT) is a decision tree classifier and uses incremental learning which is suitable for data stream mining. HAT creates decision trees on the fly from limited number of instances, uses low memory, has fast evaluation time, and adapts to concept changes. The experiments showed NNGE and HAT with STEM make effective EIDS that have high classification accuracy, low false positives, low memory usage, and fast classification times

Event and Intrusion Detection Systems for Cyber-Physical Power Systems

High speed data from Wide Area Measurement Systems (WAMS) with Phasor Measurement Units (PMU) enables real and non-real time monitoring and control of power systems. The information and communication infrastructure used in WAMS efficiently transports information but introduces cyber security vulnerabilities. Adversaries may exploit such vulnerabilities to create cyber-attacks against the electric power grid. Control centers need to be updated to be resilient not only to well-known power system contingencies but also to cyber-attacks. Therefore, a combined event and intrusion detection systems (EIDS) is required that can provide precise classification for optimal response. This dissertation describes a WAMS cyber-physical power system test bed that was developed to generate datasets and perform cyber-physical power system research related to cyber-physical system vulnerabilities, cyber-attack impact studies, and machine learning algorithms for EIDS. The test bed integrates WAMS components with a Real Time Digital Simulator (RTDS) with hardware in the loop (HIL) and includes various sized power systems with a wide variety of implemented power system and cyber-attack scenarios. This work developed a novel data processing and compression method to address the WAMS big data problem. The State Tracking and Extraction Method (STEM) tracks system states from measurements and creates a compressed sequence of states for each observed scenario. Experiments showed STEM reduces data size significantly without losing key event information in the dataset that is useful to train EIDS and classify events. Two EIDS are proposed and evaluated in this dissertation. Non-Nested Generalized Exemplars (NNGE) is a rule based classifier that creates rules in the form of hyperrectangles to classify events. NNGE uses rule generalization to create a model that has high accuracy and fast classification time. Hoeffding adaptive trees (HAT) is a decision tree classifier and uses incremental learning which is suitable for data stream mining. HAT creates decision trees on the fly from limited number of instances, uses low memory, has fast evaluation time, and adapts to concept changes. The experiments showed NNGE and HAT with STEM make effective EIDS that have high classification accuracy, low false positives, low memory usage, and fast classification times

Virus Metagenomics in Farm Animals: A Systematic Review

A majority of emerging infectious diseases are of zoonotic origin. Metagenomic Next-Generation Sequencing (mNGS) has been employed to identify uncommon and novel infectious etiologies and characterize virus diversity in human, animal, and environmental samples. Here, we systematically reviewed studies that performed viral mNGS in common livestock (cattle, small ruminants, poultry, and pigs). We identified 2481 records and 120 records were ultimately included after a first and second screening. Pigs were the most frequently studied livestock and the virus diversity found in samples from poultry was the highest. Known animal viruses, zoonotic viruses, and novel viruses were reported in available literature, demonstrating the capacity of mNGS to identify both known and novel viruses. However, the coverage of metagenomic studies was patchy, with few data on the virome of small ruminants and respiratory virome of studied livestock. Essential metadata such as age of livestock and farm types were rarely mentioned in available literature, and only 10.8% of the datasets were publicly available. Developing a deeper understanding of livestock virome is crucial for detection of potential zoonotic and animal pathogens and One Health preparedness. Metagenomic studies can provide this background but only when combined with essential metadata and following the "FAIR" (Findable, Accessible, Interoperable, and Reusable) data principles

Intrusion detection in IPv6-enabled sensor networks.

In this research, we study efficient and lightweight Intrusion Detection Systems (IDS) for ad-hoc networks through the lens of IPv6-enabled Wireless Sensor Actuator Networks. These networks consist of highly constrained devices able to communicate wirelessly in an ad-hoc fashion, thus following the architecture of ad-hoc networks. Current state of the art IDS in IoT and WSNs have been developed considering the architecture of conventional computer networks, and as such they do not efficiently address the paradigm of ad-hoc networks, which is highly relevant in emerging network paradigms, such as the Internet of Things (IoT). In this context, the network properties of resilience and redundancy have not been extensively studied. In this thesis, we first identify a trade-off between the communication and energy overheads of an IDS (as captured by the number of active IDS agents in the network) and the performance of the system in terms of successfully identifying attacks. In order to fine-tune this trade-off, we model networks as Random Geometric Graphs; these are a rigorous approach that allows us to capture underlying structural properties of the network. We then introduce a novel IDS architectural approach that consists of a central IDS agent and set of distributed IDS agents deployed uniformly at random over the network area. These nodes are able to efficiently detect attacks at the networking layer in a collaborative manner by monitoring locally available network information provided by IoT routing protocols, such as RPL. The detailed experimental evaluation conducted in this research demonstrates significant performance gains in terms of communication overhead and energy dissipation while maintaining high detection rates. We also show that the performance of our IDS in ad-hoc networks does not rely on the size of the network but on fundamental underling network properties, such as the network topology and the average degree of the nodes. The experiments show that our proposed IDS architecture is resilient against frequent topology changes due to node failures

Social and Legal Implications of Digital Identity in a Multi-national Environment

The i2010 e-Government Action Plan from the European Union forces Public Administrations (national, regional and local) of all Member States that by 2010 to meet all administrative acts of the citizens through the Internet. This implies the need for mechanisms and systems to be able to unequivocally identify people on the Internet, together with a reliable system of interoperable electronic identification management (eIDM), in such a way that citizens, businesses and government departments (even in different Member States) can identify themselves and certify their transactions accurately, quickly and simply. However, despite the clear advantages that this entails for EU citizens, namely the fact that they possess a digital identity which allows them secure and identified access to the services offered by the various public administrations in Member States, the implementation of a solution of this kind involves a series of risks which, if they are not duly dealt with, may engender a reduction in the effectiveness of public institutions and citizens' trust in them. This article will analyse the problems associated with digital identity in the EU framework and the extent to which the solutions adopted to date meet the constitutional requirements, or fail to, highlighting aspects which may entail a risk or detriment to the freedoms of citizens and those relating to the handling of digital identity which have not yet been tackled but which, given their particular relevance, necessitate an immediate solution