MevaL: A Visual Machine Learning Model Evaluation Tool for Financial Crime Detection

Data Science and Machine Learning are two valuable allies to fight financial crime,the domain where Feedzai seeks to leverage its value proposition in support of its mission:to make banking and commerce safe. Data is at the core of both fields and this domain, sostructuring instances for visual consumption provides an effective way of understandingthe data and communicating insights.The development of a solution for each project and use case requires a careful andeffective Machine Learning Model Evaluation stage, as it is the major source of feedbackbefore deployment. The tooling for this stage available at Feedzai can be improved,accelerated, visually supported, and diversified to enable data scientists to boost theirdaily work and the quality of the models.In this work, I propose to collect and compile internal and external input, in terms ofworkflow and Model Evaluation, in a proposal hierarchically segmented by well-definedobjectives and tasks, to instantiate the proposal in a Python package, and to iteratively val-idate the package with Feedzai’s data scientists. Therefore, the first contribution is MevaL,a Python package for Model Evaluation with visual support, integrated into Feedzai’s DataScience environment by design. In fact, MevaL is already being leveraged as a visualization package on two internal reporting projects that are serving some of Feedzai’s majorclients.In addition to MevaL, the second contribution of this work is the Model EvaluationTopology developed to ensure clear communication and design of features.A Ciência de Dados e a Aprendizagem Automática [277] são duas valiosas aliadas no combate à criminalidade económico-financeira, o domínio em que a Feedzai procura potenciar a sua proposta de valor em prol da sua missão: tornar o sistema bancário e o comércio seguros. Além disso, os dados estão no centro das duas áreas e deste domínio.Assim, a estruturação visual dos mesmos fornece uma maneira eficaz de os entender e transmitir informação.O desenvolvimento de uma solução para cada projeto e caso de uso requer um estágiocuidadoso e eficaz de Avaliação de Modelos de Aprendizagem Automática, pois esteestágio coincide com a principal fonte de retorno (feedback) antes da implementaçãoda solução. As ferramentas de Avaliação de Modelos disponíveis na Feedzai podem seraprimoradas, aceleradas, suportadas visualmente e diversificadas para permitir que oscientistas de dados impulsionem o seu trabalho diário e a qualidade destes modelos.Neste trabalho, proponho a recolha e compilação de informação interna e externa, em termos de fluxo de trabalho e Avaliação de Modelos, numa proposta hierarquicamente segmentada por objetivos e tarefas bem definidas, a instanciação desta proposta num pacote Python e a validação iterativa deste pacote em colaboração com os cientistas de dados da Feedzai. Posto isto, a primeira contribuição deste trabalho é o MevaL, um pacote Python para Avaliação de Modelos com suporte visual, integrado no ambiente de Ciência de Dados da Feedzai. Na verdade, o MevaL já está a ser utilizado como um pacote de visualização em dois projetos internos de preparação de relatórios automáticos para alguns dos principais clientes da Feedzai.Além do MevaL, a segunda contribuição deste trabalho é a Topologia de Avaliação de Modelos desenvolvida para garantir uma comunicação clara e o design enquadrado das diferentes funcionalidades

Cyber situational Awareness Dashboard for information security

Numa era em que organizações e pessoas vivem interconectados num mundo cibernético e, adivinhando-se que com as novas tecnologias emergentes, a produção de dados digitais e comunicações entre diferentes sistemas e entidades aumenta consideravelmente, torna-se cada vez mais premente a disponibilização e implementação de sistemas capazes de, não só assegurar a segurança digital, como medi-la e quantifica-la face às necessidades intrínsecas de cada entidade. O objetivo deste estudo é a criação de um dashboard de consciência situacional baseado na identificação do estado da arte relativamente às métricas de segurança de informação e arquiteturas que suportem a implementação de um sistema de consciência situacional. A metodologia de estudo utilizada foi descritiva com foco quantitativo. O produto conceptualizado, projetado e implementado nesta dissertação teve como base a utilização de um software comercial, amplamente adotado no contexto empresarial. A definição de métricas foi efetuada à medida para o caso de estudo académico, sendo expansível e permitindo desde o início da sua implementação dar resposta e assegurar a consciência situacional de potenciais utilizadores face às necessidades de uma organização. A utilização do produto desenvolvido nesta dissertação permite futuras integrações com sistemas de análise preditiva que permitam melhorar a eficiência dos sistemas de segurança de informação

Accessibility and Inclusion in Learning Management System Design: Creating an Online Learning Platform for Lifelong Learners

abstract: As the impact of technology on daily life continues to grow, online learning platforms for primary, secondary, post-secondary, and professional institutions find ways to: 1. Connect peers and instructors through digital communication, 2. Engage users more fully in learning, and 3. Provide access to resources that enhance deep-impact education. Online learning platforms, or learning management systems (LMS), are used to connect instructors and students through synchronous and asynchronous engagement tools, provide space for the transfer of resources and ideas, and track progress. However, these platforms were designed with more mainstream purposes - and more digitally savvy - users in mind. Adult learning programs (with members ages 50+) currently have no online learning and sharing platform specifically designed to fit the needs and desires of their users. Despite the multitude of barriers to successful use, adult learning programs recognize the need to engage with members digitally and are seeking an online learning platform centered around their users. This project, utilizing best practices in technical communication and mixed methods user experience research, broadens the boundaries of communication design by creating an online learning platform prototype specifically for adults ages 50+ through the lens of information design, content management, and user experience outcomes.Master's Applied Projec

A Data-driven, High-performance and Intelligent CyberInfrastructure to Advance Spatial Sciences

abstract: In the field of Geographic Information Science (GIScience), we have witnessed the unprecedented data deluge brought about by the rapid advancement of high-resolution data observing technologies. For example, with the advancement of Earth Observation (EO) technologies, a massive amount of EO data including remote sensing data and other sensor observation data about earthquake, climate, ocean, hydrology, volcano, glacier, etc., are being collected on a daily basis by a wide range of organizations. In addition to the observation data, human-generated data including microblogs, photos, consumption records, evaluations, unstructured webpages and other Volunteered Geographical Information (VGI) are incessantly generated and shared on the Internet. Meanwhile, the emerging cyberinfrastructure rapidly increases our capacity for handling such massive data with regard to data collection and management, data integration and interoperability, data transmission and visualization, high-performance computing, etc. Cyberinfrastructure (CI) consists of computing systems, data storage systems, advanced instruments and data repositories, visualization environments, and people, all linked together by software and high-performance networks to improve research productivity and enable breakthroughs that are not otherwise possible. The Geospatial CI (GCI, or CyberGIS), as the synthesis of CI and GIScience has inherent advantages in enabling computationally intensive spatial analysis and modeling (SAM) and collaborative geospatial problem solving and decision making. This dissertation is dedicated to addressing several critical issues and improving the performance of existing methodologies and systems in the field of CyberGIS. My dissertation will include three parts: The first part is focused on developing methodologies to help public researchers find appropriate open geo-spatial datasets from millions of records provided by thousands of organizations scattered around the world efficiently and effectively. Machine learning and semantic search methods will be utilized in this research. The second part develops an interoperable and replicable geoprocessing service by synthesizing the high-performance computing (HPC) environment, the core spatial statistic/analysis algorithms from the widely adopted open source python package – Python Spatial Analysis Library (PySAL), and rich datasets acquired from the first research. The third part is dedicated to studying optimization strategies for feature data transmission and visualization. This study is intended for solving the performance issue in large feature data transmission through the Internet and visualization on the client (browser) side. Taken together, the three parts constitute an endeavor towards the methodological improvement and implementation practice of the data-driven, high-performance and intelligent CI to advance spatial sciences.Dissertation/ThesisDoctoral Dissertation Geography 201

Cyber-Physical Threat Intelligence for Critical Infrastructures Security

Modern critical infrastructures can be considered as large scale Cyber Physical Systems (CPS). Therefore, when designing, implementing, and operating systems for Critical Infrastructure Protection (CIP), the boundaries between physical security and cybersecurity are blurred. Emerging systems for Critical Infrastructures Security and Protection must therefore consider integrated approaches that emphasize the interplay between cybersecurity and physical security techniques. Hence, there is a need for a new type of integrated security intelligence i.e., Cyber-Physical Threat Intelligence (CPTI). This book presents novel solutions for integrated Cyber-Physical Threat Intelligence for infrastructures in various sectors, such as Industrial Sites and Plants, Air Transport, Gas, Healthcare, and Finance. The solutions rely on novel methods and technologies, such as integrated modelling for cyber-physical systems, novel reliance indicators, and data driven approaches including BigData analytics and Artificial Intelligence (AI). Some of the presented approaches are sector agnostic i.e., applicable to different sectors with a fair customization effort. Nevertheless, the book presents also peculiar challenges of specific sectors and how they can be addressed. The presented solutions consider the European policy context for Security, Cyber security, and Critical Infrastructure protection, as laid out by the European Commission (EC) to support its Member States to protect and ensure the resilience of their critical infrastructures. Most of the co-authors and contributors are from European Research and Technology Organizations, as well as from European Critical Infrastructure Operators. Hence, the presented solutions respect the European approach to CIP, as reflected in the pillars of the European policy framework. The latter includes for example the Directive on security of network and information systems (NIS Directive), the Directive on protecting European Critical Infrastructures, the General Data Protection Regulation (GDPR), and the Cybersecurity Act Regulation. The sector specific solutions that are described in the book have been developed and validated in the scope of several European Commission (EC) co-funded projects on Critical Infrastructure Protection (CIP), which focus on the listed sectors. Overall, the book illustrates a rich set of systems, technologies, and applications that critical infrastructure operators could consult to shape their future strategies. It also provides a catalogue of CPTI case studies in different sectors, which could be useful for security consultants and practitioners as well

Teaching analytics and teacher dashboards to visualise SET data: Implication to theory and practice

Teaching Analytics (TA) is an emergent theoretical approach that combines teaching expertise, visual analytics, and design-based research to support teachers' diagnostic pedagogical ability to use data as evidence to improve teaching quality. The thesis is focused on designing dashboards to help teachers visualise Student Evaluation of Teaching (SET) data as a form of TA for improving the quality of teaching. The research examined the role of TA by deploying customisable dashboards to support teachers in using data to design and facilitate learning. The researcher carried out an integrated literature review to explore the notion of TA and SET data. Moreover, a Data Science Life Cycle model was proposed to guide teachers and researchers using SET data to improve learning and teaching quality. The research comprised several phases. In phase I, a simulated data technique was used to generate SET scores that informed the development of a preliminary teacher dashboard. Phase II surveyed teachers' use of SET data. The survey results indicated that more than half of the participants used SET for improving teaching practice. The research also showed that participants valued the free-text qualitative comments in SET data. Hence, phase III collected real free-text qualitative comments in SET data on students' perceptions of a previously tutored course. The survey results further indicated that although teachers were unaware of a dashboard's value in presenting data, they wanted to visualise SET data using dashboards. Phase IV redesigned the preliminary dashboards to present the real SET data and the simulated SET scores. Finally, phase V carried out usability testing to evaluate teachers' perceptions of usability and usefulness of the teacher's dashboards. Overall, the result of the usability study indicated the perceived value of the teacher's dashboards

Social media platforms as economic organisations: reconstructing the evolution of TripAdvisor’s operations

Social media platforms have gradually become embedded in the digital economy. During this process, their original identity as sites that exclusively facilitated content sharing and user networking has changed significantly. The so-called “social media” literature has traditionally centred on user’s capabilities to networking, sharing content and building community. The “digital platforms” literature has focused on the rules, conditions and governance of resource exchanges between multiples sides of a platform. The “ecosystem literature” has focused on understanding the emergence of a complex web of commercial relationships that prompt value creation. Meaningful as they are, current literature tends to treat technology as a black box by overlooking the ways it moulds perceptions, attitudes, relations, and actions. This study fills this gap by critically examining the role of data and technology in social media platforms. It argues that social media form part of the digital economy by developing both technological and organisational capability to exploit data systematically. The thesis advances these ideas through the study of TripAdvisor from its creation in 2000 through 2019. It uses qualitative analysis complemented with digital methods. The analysis reveals several stages in TripAdvisor’s evolution. Each stage is closely related to TripAdvisor’s capabilities and strategies for procuring, producing and exchanging data with its ecosystem. The thesis develops a general framework that contributes to uncovering the technological, organisational, and economic complexities involved in the evolution of social media. It explains the structural transformations of social media platforms and their current embeddedness in webs of relationships, characteristic of the digital economy. The thesis suggests that the platform’s identity is the result of an intricate interplay of strategies, technology, actors, relationships and practices that influence and reinforce one another

Cyber-Physical Threat Intelligence for Critical Infrastructures Security

Modern critical infrastructures can be considered as large scale Cyber Physical Systems (CPS). Therefore, when designing, implementing, and operating systems for Critical Infrastructure Protection (CIP), the boundaries between physical security and cybersecurity are blurred. Emerging systems for Critical Infrastructures Security and Protection must therefore consider integrated approaches that emphasize the interplay between cybersecurity and physical security techniques. Hence, there is a need for a new type of integrated security intelligence i.e., Cyber-Physical Threat Intelligence (CPTI). This book presents novel solutions for integrated Cyber-Physical Threat Intelligence for infrastructures in various sectors, such as Industrial Sites and Plants, Air Transport, Gas, Healthcare, and Finance. The solutions rely on novel methods and technologies, such as integrated modelling for cyber-physical systems, novel reliance indicators, and data driven approaches including BigData analytics and Artificial Intelligence (AI). Some of the presented approaches are sector agnostic i.e., applicable to different sectors with a fair customization effort. Nevertheless, the book presents also peculiar challenges of specific sectors and how they can be addressed. The presented solutions consider the European policy context for Security, Cyber security, and Critical Infrastructure protection, as laid out by the European Commission (EC) to support its Member States to protect and ensure the resilience of their critical infrastructures. Most of the co-authors and contributors are from European Research and Technology Organizations, as well as from European Critical Infrastructure Operators. Hence, the presented solutions respect the European approach to CIP, as reflected in the pillars of the European policy framework. The latter includes for example the Directive on security of network and information systems (NIS Directive), the Directive on protecting European Critical Infrastructures, the General Data Protection Regulation (GDPR), and the Cybersecurity Act Regulation. The sector specific solutions that are described in the book have been developed and validated in the scope of several European Commission (EC) co-funded projects on Critical Infrastructure Protection (CIP), which focus on the listed sectors. Overall, the book illustrates a rich set of systems, technologies, and applications that critical infrastructure operators could consult to shape their future strategies. It also provides a catalogue of CPTI case studies in different sectors, which could be useful for security consultants and practitioners as well

NETWORK TRAFFIC CHARACTERIZATION AND INTRUSION DETECTION IN BUILDING AUTOMATION SYSTEMS

The goal of this research was threefold: (1) to learn the operational trends and behaviors of a realworld building automation system (BAS) network for creating building device models to detect anomalous behaviors and attacks, (2) to design a framework for evaluating BA device security from both the device and network perspectives, and (3) to leverage new sources of building automation device documentation for developing robust network security rules for BAS intrusion detection systems (IDSs). These goals were achieved in three phases, first through the detailed longitudinal study and characterization of a real university campus building automation network (BAN) and with the application of machine learning techniques on field level traffic for anomaly detection. Next, through the systematization of literature in the BAS security domain to analyze cross protocol device vulnerabilities, attacks, and defenses for uncovering research gaps as the foundational basis of our proposed BA device security evaluation framework. Then, to evaluate our proposed framework the largest multiprotocol BAS testbed discussed in the literature was built and several side-channel vulnerabilities and software/firmware shortcomings were exposed. Finally, through the development of a semi-automated specification gathering, device documentation extracting, IDS rule generating framework that leveraged PICS files and BIM models.Ph.D