5,120 research outputs found

    A Survey on Forensics and Compliance Auditing for Critical Infrastructure Protection

    Get PDF
    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

    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

    A deep learning-enhanced digital twin framework for improving safety and reliability in human-robot collaborative manufacturing

    Get PDF
    In Industry 5.0, Digital Twins bring in flexibility and efficiency for smart manufacturing. Recently, the success of artificial intelligence techniques such as deep learning has led to their adoption in manufacturing and especially in human–robot collaboration. Collaborative manufacturing tasks involving human operators and robots pose significant safety and reliability concerns. In response to these concerns, a deep learning-enhanced Digital Twin framework is introduced through which human operators and robots can be detected and their actions can be classified during the manufacturing process, enabling autonomous decision making by the robot control system. Developed using Unreal Engine 4, our Digital Twin framework complies with the Robotics Operating System specification, and supports synchronous control and communication between the Digital Twin and the physical system. In our framework, a fully-supervised detector based on a faster region-based convolutional neural network is firstly trained on synthetic data generated by the Digital Twin, and then tested on the physical system to demonstrate the effectiveness of the proposed Digital Twin-based framework. To ensure safety and reliability, a semi-supervised detector is further designed to bridge the gap between the twin system and the physical system, and improved performance is achieved by the semi-supervised detector compared to the fully-supervised detector that is simply trained on either synthetic data or real data. The evaluation of the framework in multiple scenarios in which human operators collaborate with a Universal Robot 10 shows that it can accurately detect the human and robot, and classify their actions under a variety of conditions. The data from this evaluation have been made publicly available, and can be widely used for research and operational purposes. Additionally, a semi-automated annotation tool from the Digital Twin framework is published to benefit the collaborative robotics community

    The Pragmatic Development of a Carbon Management Framework for UK SMEs

    Get PDF
    The UK's commitment to net-zero emissions by 2050 is challenged by critics citing current government strategies as inadequate, marked by a lack of concrete action and aspirational guidelines. Notably, businesses, including small and medium-sized enterprises (SMEs) which constitute about half of all business emissions, are pivotal to this goal. Yet, existing policies and standards often neglect the significant role of SMEs, who face barriers such as limited knowledge and resources in implementing carbon management practices. This thesis explores the development of a novel carbon management framework specifically designed for medium-sized organisations in the UK to address these problems. The research adopts a practical approach through collaboration with an industry partner, facilitating a case study for real-world application. Adopting a mixed-methods research design grounded in pragmatism, the study commenced with a qualitative study in the form of a focus group. This exploratory phase, critical for understanding SME challenges, yielded rich data revealing key management themes in strategy, energy, and data. The framework design was supported by a materiality assessment and input from key stakeholders on three major iterations. The final framework comprises three phases: establishing a baseline carbon footprint, creating a carbon reduction plan, and strategically implementing this plan. The validation process, conducted at Knowsley Safari, successfully tested the initial two phases but faced constraints in fully assessing the third phase due to time limitations. While the research achieved its primary aim of developing a novel carbon management framework for SMEs, it encountered limitations, notably in time and the generalisability of findings due to reliance on a single case study. Future research could test the framework across diverse SME settings to establish its broader applicability and effectiveness in aiding the UK's net-zero emission goals

    Antecedents, Moderators, Mediators and Outcome of Open Innovation: A Study Among Manufacturing Firms in the UK

    Get PDF
    This thesis integrates the resource-based theory, the capability-based view, and the contingency approach to examine the key antecedents, moderators, mediators, and outcomes of open innovation. Based on a rigorous systematic literature review, using a comprehensive set of survey data from 206 UK manufacturing firms, this thesis integrates three interrelated papers on open innovation. The first paper examines the current state of knowledge in open innovation literature. Nine hundred and forty-four (944) articles from leading journals on open innovation were reviewed and synthesised. Overall, the findings identify common themes in the literature and highlight research gaps that, if pursued, could enrich the literature. The second paper examines the influence of technological capability and marketing capability on inbound and outbound open innovation, and the moderating effect of government support. The study shows that technological capability enhances inbound and outbound open innovation, while marketing capability hinders inbound and outbound open innovation. In addition, the study shows that the interaction of government support and technological capability is significant and positive for inbound open innovation, but insignificant for outbound open innovation. Furthermore, the interaction of government support and marketing capability is significant and negative for both inbound and outbound open innovation. The third paper examines the internal mechanisms between inbound and outbound open innovation on firm performance. It was found that both inbound and outbound open innovation were not significantly related to firm performance. In addition, strategic flexibility negatively mediated the relationship between outbound open innovation and firm performance, while innovation performance did not mediate this relationship. Furthermore, strategic flexibility and innovation performance were serial mediators in the relationship between outbound open innovation and firm performance. In addition, organisational relearning positively moderated the relationship between inbound open innovation and firm performance

    A Deep Learning-enhanced Digital Twin Framework for Improving Safety and Reliability in Human-Robot Collaborative Manufacturing

    Get PDF
    In Industry 5.0, Digital Twins bring in flexibility and efficiency for smart manufacturing. Recently, the success of artificial intelligence techniques such as deep learning has led to their adoption in manufacturing and especially in human–robot collaboration. Collaborative manufacturing tasks involving human operators and robots pose significant safety and reliability concerns. In response to these concerns, a deep learning-enhanced Digital Twin framework is introduced through which human operators and robots can be detected and their actions can be classified during the manufacturing process, enabling autonomous decision making by the robot control system. Developed using Unreal Engine 4, our Digital Twin framework complies with the Robotics Operating System specification, and supports synchronous control and communication between the Digital Twin and the physical system. In our framework, a fully-supervised detector based on a faster region-based convolutional neural network is firstly trained on synthetic data generated by the Digital Twin, and then tested on the physical system to demonstrate the effectiveness of the proposed Digital Twin-based framework. To ensure safety and reliability, a semi-supervised detector is further designed to bridge the gap between the twin system and the physical system, and improved performance is achieved by the semi-supervised detector compared to the fully-supervised detector that is simply trained on either synthetic data or real data. The evaluation of the framework in multiple scenarios in which human operators collaborate with a Universal Robot 10 shows that it can accurately detect the human and robot, and classify their actions under a variety of conditions. The data from this evaluation have been made publicly available, and can be widely used for research and operational purposes. Additionally, a semi-automated annotation tool from the Digital Twin framework is published to benefit the collaborative robotics community

    Digitalization and Development

    Get PDF
    This book examines the diffusion of digitalization and Industry 4.0 technologies in Malaysia by focusing on the ecosystem critical for its expansion. The chapters examine the digital proliferation in major sectors of agriculture, manufacturing, e-commerce and services, as well as the intermediary organizations essential for the orderly performance of socioeconomic agents. The book incisively reviews policy instruments critical for the effective and orderly development of the embedding organizations, and the regulatory framework needed to quicken the appropriation of socioeconomic synergies from digitalization and Industry 4.0 technologies. It highlights the importance of collaboration between government, academic and industry partners, as well as makes key recommendations on how to encourage adoption of IR4.0 technologies in the short- and long-term. This book bridges the concepts and applications of digitalization and Industry 4.0 and will be a must-read for policy makers seeking to quicken the adoption of its technologies

    Systemic Circular Economy Solutions for Fiber Reinforced Composites

    Get PDF
    This open access book provides an overview of the work undertaken within the FiberEUse project, which developed solutions enhancing the profitability of composite recycling and reuse in value-added products, with a cross-sectorial approach. Glass and carbon fiber reinforced polymers, or composites, are increasingly used as structural materials in many manufacturing sectors like transport, constructions and energy due to their better lightweight and corrosion resistance compared to metals. However, composite recycling is still a challenge since no significant added value in the recycling and reprocessing of composites is demonstrated. FiberEUse developed innovative solutions and business models towards sustainable Circular Economy solutions for post-use composite-made products. Three strategies are presented, namely mechanical recycling of short fibers, thermal recycling of long fibers and modular car parts design for sustainable disassembly and remanufacturing. The validation of the FiberEUse approach within eight industrial demonstrators shows the potentials towards new Circular Economy value-chains for composite materials

    A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery

    Full text link
    Semantic segmentation (classification) of Earth Observation imagery is a crucial task in remote sensing. This paper presents a comprehensive review of technical factors to consider when designing neural networks for this purpose. The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer models, discussing prominent design patterns for these ANN families and their implications for semantic segmentation. Common pre-processing techniques for ensuring optimal data preparation are also covered. These include methods for image normalization and chipping, as well as strategies for addressing data imbalance in training samples, and techniques for overcoming limited data, including augmentation techniques, transfer learning, and domain adaptation. By encompassing both the technical aspects of neural network design and the data-related considerations, this review provides researchers and practitioners with a comprehensive and up-to-date understanding of the factors involved in designing effective neural networks for semantic segmentation of Earth Observation imagery.Comment: 145 pages with 32 figure
    • …
    corecore