4,020 research outputs found

    A forensics and compliance auditing framework for critical infrastructure protection

    Get PDF
    Contemporary societies are increasingly dependent on products and services provided by Critical Infrastructure (CI) such as power plants, energy distribution networks, transportation systems and manufacturing facilities. Due to their nature, size and complexity, such CIs are often supported by Industrial Automation and Control Systems (IACS), which are in charge of managing assets and controlling everyday operations. As these IACS become larger and more complex, encompassing a growing number of processes and interconnected monitoring and actuating devices, the attack surface of the underlying CIs increases. This situation calls for new strategies to improve Critical Infrastructure Protection (CIP) frameworks, based on evolved approaches for data analytics, able to gather insights from the CI. In this paper, we propose an Intrusion and Anomaly Detection System (IADS) framework that adopts forensics and compliance auditing capabilities at its core to improve CIP. Adopted forensics techniques help to address, for instance, post-incident analysis and investigation, while the support of continuous auditing processes simplifies compliance management and service quality assessment. More specifically, after discussing the rationale for such a framework, this paper presents a formal description of the proposed components and functions and discusses how the framework can be implemented using a cloud-native approach, to address both functional and non-functional requirements. An experimental analysis of the framework scalability is also provided.info:eu-repo/semantics/publishedVersio

    Computational and experimental studies on the reaction mechanism of bio-oil components with additives for increased stability and fuel quality

    Get PDF
    As one of the world’s largest palm oil producers, Malaysia encountered a major disposal problem as vast amount of oil palm biomass wastes are produced. To overcome this problem, these biomass wastes can be liquefied into biofuel with fast pyrolysis technology. However, further upgradation of fast pyrolysis bio-oil via direct solvent addition was required to overcome it’s undesirable attributes. In addition, the high production cost of biofuels often hinders its commercialisation. Thus, the designed solvent-oil blend needs to achieve both fuel functionality and economic targets to be competitive with the conventional diesel fuel. In this thesis, a multi-stage computer-aided molecular design (CAMD) framework was employed for bio-oil solvent design. In the design problem, molecular signature descriptors were applied to accommodate different classes of property prediction models. However, the complexity of the CAMD problem increases as the height of signature increases due to the combinatorial nature of higher order signature. Thus, a consistency rule was developed reduce the size of the CAMD problem. The CAMD problem was then further extended to address the economic aspects via fuzzy multi-objective optimisation approach. Next, a rough-set based machine learning (RSML) model has been proposed to correlate the feedstock characterisation and pyrolysis condition with the pyrolysis bio-oil properties by generating decision rules. The generated decision rules were analysed from a scientific standpoint to identify the underlying patterns, while ensuring the rules were logical. The decision rules generated can be used to select optimal feedstock composition and pyrolysis condition to produce pyrolysis bio-oil of targeted fuel properties. Next, the results obtained from the computational approaches were verified through experimental study. The generated pyrolysis bio-oils were blended with the identified solvents at various mixing ratio. In addition, emulsification of the solvent-oil blend in diesel was also conducted with the help of surfactants. Lastly, potential extensions and prospective work for this study have been discuss in the later part of this thesis. To conclude, this thesis presented the combination of computational and experimental approaches in upgrading the fuel properties of pyrolysis bio-oil. As a result, high quality biofuel can be generated as a cleaner burning replacement for conventional diesel fuel

    The Complexity of Community: Intervillage Migrations in the Illinois Country, 1699-1763

    Get PDF
    This Master’s thesis examines how social networks impacted the changing composition of French settler villages in the Illinois Country (present day Illinois and Missouri, USA), 1699-1763. More specifically, it analyzes the linkages between French settlers, Indigenous Illinois communities, and both enslaved Black and Indigenous peoples during a foundational period of migration and settlement. Rather than examining migration patterns to and from the Illinois Country, the focus of this thesis lies in examining intervillage migrations between six Illinois villages (Kaskaskia, Fort de Chartres, Prairie du Rocher, St. Philippe, Cahokia, and Ste. Geneviùve). Though these villages have typically been portrayed as becoming increasingly French in character throughout the first half of the eighteenth century, this project rethinks the process of colonization by rethinking the ways in which intervillage migration created and maintained dynamic centres of exchange and cross-cultural contact. More broadly, the project contributes to a revaluation of the nature of colonialism for vast portions of French North America throughout the 18th century

    Perspectives of Hispanic/Latina Women Ages 60 and Over on the Impact of Single Motherhood and Their Long-Term Financial Well-Being

    Get PDF
    Unmarried women over the age of 60 continue to experience disproportionate rates of adult poverty in the United States, while families headed by single mothers experience the highest poverty rates. This study explores the long-term impact of single motherhood on financial wellness through the perspective of Hispanic/Latina women ages 60 and over who have experienced single motherhood in Massachusetts. A transdisciplinary study, it utilizes intersectionality as a theoretical framework, employs feminist standpoint informed inquiry methods to document lived experiences through in-depth interviews, and engages diffraction as a mode of praxis as it intra-acts with narratives and explores the systems and structures participant lives are entangled with. As it explores the perspectives and narratives of participants regarding their experiences with single motherhood and their financial well-being, this research documents and shares the voices of this often neglected and excluded population. It considers the notion of single motherhood within the public imaginary, and its influence on the phenomenon and lived experience of single motherhood. In doing so it engages with impacts of single motherhood on long-term financial well-being in a way that could inform future research as well as inform the development, enhancement, and/or revision of public policies. A key finding of this research is the role of stigma and shame on financial well-being and its multifaceted entanglement with financial wellness. Stigma and shame are explored diffractively through an intersectional lens as it intra-acts with facets of participant identity such as single motherhood, race and ethnicity, and immigration status. Another finding of this research is the role of structural and systemic barriers that intra-act with participant lives and impact their financial wellness. This study considers the impact of material structures including policies and practices, as well as social systems including problematic aspects of resilience, public perceptions, and popular myths, on the lives of participants. Finally, this study highlights the need for further research into the possible links between experiencing single motherhood and rates of poverty among unmarried women, especially women of color, over the age of sixty

    Cognitive Machine Individualism in a Symbiotic Cybersecurity Policy Framework for the Preservation of Internet of Things Integrity: A Quantitative Study

    Get PDF
    This quantitative study examined the complex nature of modern cyber threats to propose the establishment of cyber as an interdisciplinary field of public policy initiated through the creation of a symbiotic cybersecurity policy framework. For the public good (and maintaining ideological balance), there must be recognition that public policies are at a transition point where the digital public square is a tangible reality that is more than a collection of technological widgets. The academic contribution of this research project is the fusion of humanistic principles with Internet of Things (IoT) technologies that alters our perception of the machine from an instrument of human engineering into a thinking peer to elevate cyber from technical esoterism into an interdisciplinary field of public policy. The contribution to the US national cybersecurity policy body of knowledge is a unified policy framework (manifested in the symbiotic cybersecurity policy triad) that could transform cybersecurity policies from network-based to entity-based. A correlation archival data design was used with the frequency of malicious software attacks as the dependent variable and diversity of intrusion techniques as the independent variable for RQ1. For RQ2, the frequency of detection events was the dependent variable and diversity of intrusion techniques was the independent variable. Self-determination Theory is the theoretical framework as the cognitive machine can recognize, self-endorse, and maintain its own identity based on a sense of self-motivation that is progressively shaped by the machine’s ability to learn. The transformation of cyber policies from technical esoterism into an interdisciplinary field of public policy starts with the recognition that the cognitive machine is an independent consumer of, advisor into, and influenced by public policy theories, philosophical constructs, and societal initiatives

    Subgroup discovery for structured target concepts

    Get PDF
    The main object of study in this thesis is subgroup discovery, a theoretical framework for finding subgroups in data—i.e., named sub-populations— whose behaviour with respect to a specified target concept is exceptional when compared to the rest of the dataset. This is a powerful tool that conveys crucial information to a human audience, but despite past advances has been limited to simple target concepts. In this work we propose algorithms that bring this framework to novel application domains. We introduce the concept of representative subgroups, which we use not only to ensure the fairness of a sub-population with regard to a sensitive trait, such as race or gender, but also to go beyond known trends in the data. For entities with additional relational information that can be encoded as a graph, we introduce a novel measure of robust connectedness which improves on established alternative measures of density; we then provide a method that uses this measure to discover which named sub-populations are more well-connected. Our contributions within subgroup discovery crescent with the introduction of kernelised subgroup discovery: a novel framework that enables the discovery of subgroups on i.i.d. target concepts with virtually any kind of structure. Importantly, our framework additionally provides a concrete and efficient tool that works out-of-the-box without any modification, apart from specifying the Gramian of a positive definite kernel. To use within kernelised subgroup discovery, but also on any other kind of kernel method, we additionally introduce a novel random walk graph kernel. Our kernel allows the fine tuning of the alignment between the vertices of the two compared graphs, during the count of the random walks, while we also propose meaningful structure-aware vertex labels to utilise this new capability. With these contributions we thoroughly extend the applicability of subgroup discovery and ultimately re-define it as a kernel method.Der Hauptgegenstand dieser Arbeit ist die Subgruppenentdeckung (Subgroup Discovery), ein theoretischer Rahmen fĂŒr das Auffinden von Subgruppen in Daten—d. h. benannte Teilpopulationen—deren Verhalten in Bezug auf ein bestimmtes Targetkonzept im Vergleich zum Rest des Datensatzes außergewöhnlich ist. Es handelt sich hierbei um ein leistungsfĂ€higes Instrument, das einem menschlichen Publikum wichtige Informationen vermittelt. Allerdings ist es trotz bisherigen Fortschritte auf einfache Targetkonzepte beschrĂ€nkt. In dieser Arbeit schlagen wir Algorithmen vor, die diesen Rahmen auf neuartige Anwendungsbereiche ĂŒbertragen. Wir fĂŒhren das Konzept der reprĂ€sentativen Untergruppen ein, mit dem wir nicht nur die Fairness einer Teilpopulation in Bezug auf ein sensibles Merkmal wie Rasse oder Geschlecht sicherstellen, sondern auch ĂŒber bekannte Trends in den Daten hinausgehen können. FĂŒr EntitĂ€ten mit zusĂ€tzlicher relationalen Information, die als Graph kodiert werden kann, fĂŒhren wir ein neuartiges Maß fĂŒr robuste Verbundenheit ein, das die etablierten alternativen Dichtemaße verbessert; anschließend stellen wir eine Methode bereit, die dieses Maß verwendet, um herauszufinden, welche benannte Teilpopulationen besser verbunden sind. Unsere BeitrĂ€ge in diesem Rahmen gipfeln in der EinfĂŒhrung der kernelisierten Subgruppenentdeckung: ein neuartiger Rahmen, der die Entdeckung von Subgruppen fĂŒr u.i.v. Targetkonzepten mit praktisch jeder Art von Struktur ermöglicht. Wichtigerweise, unser Rahmen bereitstellt zusĂ€tzlich ein konkretes und effizientes Werkzeug, das ohne jegliche Modifikation funktioniert, abgesehen von der Angabe des Gramian eines positiv definitiven Kernels. FĂŒr den Einsatz innerhalb der kernelisierten Subgruppentdeckung, aber auch fĂŒr jede andere Art von Kernel-Methode, fĂŒhren wir zusĂ€tzlich einen neuartigen Random-Walk-Graph-Kernel ein. Unser Kernel ermöglicht die Feinabstimmung der Ausrichtung zwischen den Eckpunkten der beiden unter-Vergleich-gestelltenen Graphen wĂ€hrend der ZĂ€hlung der Random Walks, wĂ€hrend wir auch sinnvolle strukturbewusste Vertex-Labels vorschlagen, um diese neue FĂ€higkeit zu nutzen. Mit diesen BeitrĂ€gen erweitern wir die Anwendbarkeit der Subgruppentdeckung grĂŒndlich und definieren wir sie im Endeffekt als Kernel-Methode neu

    The dual effect of background music on creativity: perspectives of music preference and cognitive interference

    Get PDF
    Music, an influential environmental factor, significantly shapes cognitive processing and everyday experiences, thus rendering its effects on creativity a dynamic topic within the field of cognitive science. However, debates continue about whether music bolsters, obstructs, or exerts a dual influence on individual creativity. Among the points of contention is the impact of contrasting musical emotions–both positive and negative–on creative tasks. In this study, we focused on traditional Chinese music, drawn from a culture known for its ‘preference for sadness,’ as our selected emotional stimulus and background music. This choice, underrepresented in previous research, was based on its uniqueness. We examined the effects of differing music genres (including vocal and instrumental), each characterized by a distinct emotional valence (positive or negative), on performance in the Alternative Uses Task (AUT). To conduct this study, we utilized an affective arousal paradigm, with a quiet background serving as a neutral control setting. A total of 114 participants were randomly assigned to three distinct groups after completing a music preference questionnaire: instrumental, vocal, and silent. Our findings showed that when compared to a quiet environment, both instrumental and vocal music as background stimuli significantly affected AUT performance. Notably, music with a negative emotional charge bolstered individual originality in creative performance. These results lend support to the dual role of background music in creativity, with instrumental music appearing to enhance creativity through factors such as emotional arousal, cognitive interference, music preference, and psychological restoration. This study challenges conventional understanding that only positive background music boosts creativity and provides empirical validation for the two-path model (positive and negative) of emotional influence on creativity
    • 

    corecore