1,078 research outputs found

    Cyber-Physical Threat Intelligence for Critical Infrastructures Security

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    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

    Cyber-Physical Threat Intelligence for Critical Infrastructures Security

    Get PDF
    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

    Mobile Augmented Reality: User Interfaces, Frameworks, and Intelligence

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    Mobile Augmented Reality (MAR) integrates computer-generated virtual objects with physical environments for mobile devices. MAR systems enable users to interact with MAR devices, such as smartphones and head-worn wearables, and perform seamless transitions from the physical world to a mixed world with digital entities. These MAR systems support user experiences using MAR devices to provide universal access to digital content. Over the past 20 years, several MAR systems have been developed, however, the studies and design of MAR frameworks have not yet been systematically reviewed from the perspective of user-centric design. This article presents the first effort of surveying existing MAR frameworks (count: 37) and further discuss the latest studies on MAR through a top-down approach: (1) MAR applications; (2) MAR visualisation techniques adaptive to user mobility and contexts; (3) systematic evaluation of MAR frameworks, including supported platforms and corresponding features such as tracking, feature extraction, and sensing capabilities; and (4) underlying machine learning approaches supporting intelligent operations within MAR systems. Finally, we summarise the development of emerging research fields and the current state-of-the-art, and discuss the important open challenges and possible theoretical and technical directions. This survey aims to benefit both researchers and MAR system developers alike.Peer reviewe

    Source term estimation of a hazardous airborne release using an unmanned aerial vehicle

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    Gaining information about an unknown gas source is a task of great importance with applications in several areas including: responding to gas leaks or suspicious smells, quantifying sources of emissions, or in an emergency response to an industrial accident or act of terrorism. In this paper, a method to estimate the source term of a gaseous release using measurements of concentration obtained from an unmanned aerial vehicle (UAV) is described. The source term parameters estimated include the three dimensional location of the release, its emission rate, and other important variables needed to forecast the spread of the gas using an atmospheric transport and dispersion model. The parameters of the source are estimated by fusing concentration observations from a gas detector on-board the aircraft, with meteorological data and an appropriate model of dispersion. Two models are compared in this paper, both derived from analytical solutions to the advection diffusion equation. Bayes’ theorem, implemented using a sequential Monte Carlo algorithm, is used to estimate the source parameters in order to take into account the large uncertainties in the observations and formulated models. The system is verified with novel, outdoor, fully automated experiments, where observations from the UAV are used to estimate the parameters of a diffusive source. The estimation performance of the algorithm is assessed subject to various flight path configurations and wind speeds. Observations and lessons learned during these unique experiments are discussed and areas for future research are identified

    Complex online harms and the smart home:A scoping review

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    Background:Technological advances in the smart home have created new opportunities for supporting digital citizens’ well-being and facilitating their empowerment but have enabled new types of complex online harms to develop. Recent statistics have indicated that ‘smart’ technology ownership increases yearly, driven by lower costs and increased accessibility. Research on smart homes has also grown, focusing on technology perspectives at the expense of a user-centric approach sensitive to the smart home’s harms, risks, and vulnerabilities.Objective:This scoping review addresses the information gap by underscoring the scope of literature that exists regarding complex online harms, vulnerabilities, and risks associated with smart home technologies and citizens’ agency. The goal is to understand the state of knowledge, gaps in the literature, and areas for future study. The importance and originality of this paper lie in its interdisciplinary review and approach. It is hoped that this research will contribute to a deeper understanding of complex online harms in the smart home.Design:Three online databases were utilised to identify papers published between 2017 and 2022, from which we selected 235 publications written in English that addressed harms, risks, vulnerabilities, and agency in the smart home context. This allowed us to map contemporary literature to reveal significant gaps in our understanding of the complex online harms affecting smart home users and identify opportunities for further research.Results:This review identified emerging themes of ‘risks’, ‘vulnerabilities’, and ‘harms’ in that order of frequency within the literature on smart homes. The usage of terms is skewed towards computing science and information security, which comprised the majority of the literature at 54.6%. Human–computer interaction papers contributed 24.4%, while social sciences accounted for 16.2%.Conclusion:Risks, harms and vulnerabilities within smart home ecosystems and IoTs are ongoing issues with complexities that necessitate research. Privacy, security, and well-being are key themes that embody the scope of complex harms affecting smart home devices in the broad literature. This review establishes disciplinary research gaps, especially in user-centred perspectives, due to a heavy technology focus in the existing literature. Therefore, further research is needed to address emergent risks, harms and vulnerabilities of smart home devices and understand how user agency and autonomy can complement the design, interface, and socio-technical aspects of smart home systems

    Video Surveillance-Based Intelligent Traffic Management in Smart Cities

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    Visualization of video is considered as important part of visual analytics. Several challenges arise from massive video contents that can be resolved by using data analytics and consequently gaining significance. Though rapid progression in digital technologies resulted in videos data explosion that incites the requirements to create visualization and computer graphics from videos, a state-of-the-art algorithm has been proposed in this chapter for 3D conversion of traffic video contents and displaying on Google Maps. Time stamped visualization based on glyph is employed efficiently in surveillance videos and utilized for event detection. This method of visualization can possibly decrease the complexity of data, having complete view of videos from video collection. The effectiveness of proposed system has shown by obtaining numerous unprocessed videos and algorithm is tested on these videos without concerning field conditions. The proposed visualization technique produces promising results and found effective in conveying meaningful information while alleviating the need of searching exhaustively colossal amount of video data

    Envisioning Digital Europe 2030: Scenarios for ICT in Future Governance and Policy Modelling

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    The report Envisioning Digital Europe 2030 is the result of research conducted by the Information Society Unit of IPTS as part of the CROSSROAD Project - A Participative Roadmap on ICT research on Electronic Governance and Policy Modelling (www.crossroad-eu.net ). After outlining the purpose and scope of the report and the methodological approach followed, the report presents the results of a systematic analysis of societal, policy and research trends in the governance and policy modelling domain in Europe. These analyses are considered central for understanding and roadmapping future research on ICT for governance and policy modelling. The study further illustrates the scenario design framework, analysing current and future challenges in ICT for governance and policy modelling, and identifying the key impact dimensions to be considered. It then presents the scenarios developed at the horizon 2030, including the illustrative storyboards representative of each scenario and the prospective opportunities and risks identified for each of them. The scenarios developed are internally consistent views of what the European governance and policy making system could have become by 2030 and of what the resulting implications for citizens, business and public services would be. Finally, the report draws conclusions and presents the proposed shared vision for Digital Europe 2030, offering also a summary of the main elements to be considered as an input for the future development of the research roadmap on ICT for governance and policy modelling.JRC.DDG.J.4-Information Societ

    Negative Multiplicity: Forecasting the Future Impact of Emerging Technologies on International Stability and Human Security

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    We asked 30 experts to forecast the developmental trajectories of twelve emerging technologies in the United States, Russia, and China until 2040 and to score their possible future impact on arms race stability, crisis stability, and humanitarian principles. The results reveal that, on average, their impact is expected to be negative, with some technologies negatively affecting all three dependent variables. We used a machine learning algorithm to cluster the technologies according to their anticipated impact. This process identified technology clusters comprised of diverse high-impact technologies that share key impact characteristics but do not necessarily share technical characteristics. We refer to these combined effects as ‘negative multiplicity’, reflecting the predominantly negative, concurrent, and in some cases similar, first- and second-order effects that emerging technologies are expected to have on international stability and human security. The expected alignment of the technology development trajectories of the United States, Russia, and China by 2040, in combination with the negative environment created by geopolitical competition, points to a nascent technological arms race that threatens to seriously impede international arms control efforts to regulate emerging technologies

    On power system automation: a Digital Twin-centric framework for the next generation of energy management systems

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    The ubiquitous digital transformation also influences power system operation. Emerging real-time applications in information (IT) and operational technology (OT) provide new opportunities to address the increasingly demanding power system operation imposed by the progressing energy transition. This IT/OT convergence is epitomised by the novel Digital Twin (DT) concept. By integrating sensor data into analytical models and aligning the model states with the observed system, a power system DT can be created. As a result, a validated high-fidelity model is derived, which can be applied within the next generation of energy management systems (EMS) to support power system operation. By providing a consistent and maintainable data model, the modular DT-centric EMS proposed in this work addresses several key requirements of modern EMS architectures. It increases the situation awareness in the control room, enables the implementation of model maintenance routines, and facilitates automation approaches, while raising the confidence into operational decisions deduced from the validated model. This gain in trust contributes to the digital transformation and enables a higher degree of power system automation. By considering operational planning and power system operation processes, a direct link to practice is ensured. The feasibility of the concept is examined by numerical case studies.The electrical power system is in the process of an extensive transformation. Driven by the energy transition towards renewable energy resources, many conventional power plants in Germany have already been decommissioned or will be decommissioned within the next decade. Among other things, these changes lead to an increased utilisation of power transmission equipment, and an increasing number of complex dynamic phenomena. The resulting system operation closer to physical boundaries leads to an increased susceptibility to disturbances, and to a reduced time span to react to critical contingencies and perturbations. In consequence, the task to operate the power system will become increasingly demanding. As some reactions to disturbances may be required within timeframes that exceed human capabilities, these developments are intrinsic drivers to enable a higher degree of automation in power system operation. This thesis proposes a framework to create a modular Digital Twin-centric energy management system. It enables the provision of validated and trustworthy models built from knowledge about the power system derived from physical laws, and process data. As the interaction of information and operational technologies is combined in the concept of the Digital Twin, it can serve as a framework for future energy management systems including novel applications for power system monitoring and control, which consider power system dynamics. To provide a validated high-fidelity dynamic power system model, time-synchronised phasor measurements of high-resolution are applied for validation and parameter estimation. This increases the trust into the underlying power system model as well as the confidence into operational decisions derived from advanced analytic applications such as online dynamic security assessment. By providing an appropriate, consistent, and maintainable data model, the framework addresses several key requirements of modern energy management system architectures, while enabling the implementation of advanced automation routines and control approaches. Future energy management systems can provide an increased observability based on the proposed architecture, whereby the situational awareness of human operators in the control room can be improved. In further development stages, cognitive systems can be applied that are able to learn from the data provided, e.g., machine learning based analytical functions. Thus, the framework enables a higher degree of power system automation, as well as the deployment of assistance and decision support functions for power system operation pointing towards a higher degree of automation in power system operation. The framework represents a contribution to the digital transformation of power system operation and facilitates a successful energy transition. The feasibility of the concept is examined by case studies in form of numerical simulations to provide a proof of concept.Das elektrische Energiesystem befindet sich in einem umfangreichen Transformations-prozess. Durch die voranschreitende Energiewende und den zunehmenden Einsatz erneuerbarer Energieträger sind in Deutschland viele konventionelle Kraftwerke bereits stillgelegt worden oder werden in den nächsten Jahren stillgelegt. Diese Veränderungen führen unter anderem zu einer erhöhten Betriebsmittelauslastung sowie zu einer verringerten Systemträgheit und somit zu einer zunehmenden Anzahl komplexer dynamischer Phänomene im elektrischen Energiesystem. Der Betrieb des Systems näher an den physikalischen Grenzen führt des Weiteren zu einer erhöhten Störanfälligkeit und zu einer verkürzten Zeitspanne, um auf kritische Ereignisse und Störungen zu reagieren. Infolgedessen wird die Aufgabe, das Stromnetz zu betreiben anspruchsvoller. Insbesondere dort wo Reaktionszeiten erforderlich sind, welche die menschlichen Fähigkeiten übersteigen sind die zuvor genannten Veränderungen intrinsische Treiber hin zu einem höheren Automatisierungsgrad in der Netzbetriebs- und Systemführung. Aufkommende Echtzeitanwendungen in den Informations- und Betriebstechnologien und eine zunehmende Menge an hochauflösenden Sensordaten ermöglichen neue Ansätze für den Entwurf und den Betrieb von cyber-physikalischen Systemen. Ein vielversprechender Ansatz, der in jüngster Zeit in diesem Zusammenhang diskutiert wurde, ist das Konzept des so genannten Digitalen Zwillings. Da das Zusammenspiel von Informations- und Betriebstechnologien im Konzept des Digitalen Zwillings vereint wird, kann es als Grundlage für eine zukünftige Leitsystemarchitektur und neuartige Anwendungen der Leittechnik herangezogen werden. In der vorliegenden Arbeit wird ein Framework entwickelt, welches einen Digitalen Zwilling in einer neuartigen modularen Leitsystemarchitektur für die Aufgabe der Überwachung und Steuerung zukünftiger Energiesysteme zweckdienlich einsetzbar macht. In Ergänzung zu den bereits vorhandenen Funktionen moderner Netzführungssysteme unterstützt das Konzept die Abbildung der Netzdynamik auf Basis eines dynamischen Netzmodells. Um eine realitätsgetreue Abbildung der Netzdynamik zu ermöglichen, werden zeitsynchrone Raumzeigermessungen für die Modellvalidierung und Modellparameterschätzung herangezogen. Dies erhöht die Aussagekraft von Sicherheitsanalysen, sowie das Vertrauen in die Modelle mit denen operative Entscheidungen generiert werden. Durch die Bereitstellung eines validierten, konsistenten und wartbaren Datenmodells auf der Grundlage von physikalischen Gesetzmäßigkeiten und während des Betriebs gewonnener Prozessdaten, adressiert der vorgestellte Architekturentwurf mehrere Schlüsselan-forderungen an moderne Netzleitsysteme. So ermöglicht das Framework einen höheren Automatisierungsgrad des Stromnetzbetriebs sowie den Einsatz von Entscheidungs-unterstützungsfunktionen bis hin zu vertrauenswürdigen Assistenzsystemen auf Basis kognitiver Systeme. Diese Funktionen können die Betriebssicherheit erhöhen und stellen einen wichtigen Beitrag zur Umsetzung der digitalen Transformation des Stromnetzbetriebs, sowie zur erfolgreichen Umsetzung der Energiewende dar. Das vorgestellte Konzept wird auf der Grundlage numerischer Simulationen untersucht, wobei die grundsätzliche Machbarkeit anhand von Fallstudien nachgewiesen wird
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