2,803 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    A Survey on Forensics and Compliance Auditing for Critical Infrastructure Protection

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    The broadening dependency and reliance that modern societies have on essential services provided by Critical Infrastructures is increasing the relevance of their trustworthiness. However, Critical Infrastructures are attractive targets for cyberattacks, due to the potential for considerable impact, not just at the economic level but also in terms of physical damage and even loss of human life. Complementing traditional security mechanisms, forensics and compliance audit processes play an important role in ensuring Critical Infrastructure trustworthiness. Compliance auditing contributes to checking if security measures are in place and compliant with standards and internal policies. Forensics assist the investigation of past security incidents. Since these two areas significantly overlap, in terms of data sources, tools and techniques, they can be merged into unified Forensics and Compliance Auditing (FCA) frameworks. In this paper, we survey the latest developments, methodologies, challenges, and solutions addressing forensics and compliance auditing in the scope of Critical Infrastructure Protection. This survey focuses on relevant contributions, capable of tackling the requirements imposed by massively distributed and complex Industrial Automation and Control Systems, in terms of handling large volumes of heterogeneous data (that can be noisy, ambiguous, and redundant) for analytic purposes, with adequate performance and reliability. The achieved results produced a taxonomy in the field of FCA whose key categories denote the relevant topics in the literature. Also, the collected knowledge resulted in the establishment of a reference FCA architecture, proposed as a generic template for a converged platform. These results are intended to guide future research on forensics and compliance auditing for Critical Infrastructure Protection.info:eu-repo/semantics/publishedVersio

    Undergraduate Catalog of Studies, 2023-2024

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    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    UMSL Bulletin 2022-2023

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    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp

    Archaeological palaeoenvironmental archives: challenges and potential

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    This Arts and Humanities Research Council (AHRC) sponsored collaborative doctoral project represents one of the most significant efforts to collate quantitative and qualitative data that can elucidate practices related to archaeological palaeoenvironmental archiving in England. The research has revealed that archived palaeoenvironmental remains are valuable resources for archaeological research and can clarify subjects that include the adoption and importation of exotic species, plant and insect invasion, human health and diet, and plant and animal husbandry practices. In addition to scientific research, archived palaeoenvironmental remains can provide evidence-based narratives of human resilience and climate change and offer evidence of the scientific process, making them ideal resources for public science engagement. These areas of potential have been realised at an imperative time; given that waterlogged palaeoenvironmental remains at significant sites such as Star Carr, Must Farm, and Flag Fen, archaeological deposits in towns and cities are at risk of decay due to climate change-related factors, and unsustainable agricultural practices. Innovative approaches to collecting and archiving palaeoenvironmental remains and maintaining existing archives will permit the creation of an accessible and thorough national resource that can service archaeologists and researchers in the related fields of biology and natural history. Furthermore, a concerted effort to recognise absences in archaeological archives, matched by an effort to supply these deficiencies, can produce a resource that can contribute to an enduring geographical and temporal record of England's biodiversity, which can be used in perpetuity in the face of diminishing archaeological and contemporary natural resources. To realise these opportunities, particular challenges must be overcome. The most prominent of these include inconsistent collection policies resulting from pressures associated with shortages in storage capacity and declining specialist knowledge in museums and repositories combined with variable curation practices. Many of these challenges can be resolved by developing a dedicated storage facility that can focus on the ongoing conservation and curation of palaeoenvironmental remains. Combined with an OASIS + module designed to handle and disseminate data pertaining to palaeoenvironmental archives, remains would be findable, accessible, and interoperable with biological archives and collections worldwide. Providing a national centre for curating palaeoenvironmental remains and a dedicated digital repository will require significant funding. Funding sources could be identified through collaboration with other disciplines. If sufficient funding cannot be identified, options that would require less financial investment, such as high-level archive audits and the production of guidance documents, will be able to assist all stakeholders with the improved curation, management, and promotion of the archived resource

    Unveiling the frontiers of deep learning: innovations shaping diverse domains

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    Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table

    Undergraduate Catalog of Studies, 2022-2023

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