11,177 research outputs found

    The Viability and Potential Consequences of IoT-Based Ransomware

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    With the increased threat of ransomware and the substantial growth of the Internet of Things (IoT) market, there is significant motivation for attackers to carry out IoT-based ransomware campaigns. In this thesis, the viability of such malware is tested. As part of this work, various techniques that could be used by ransomware developers to attack commercial IoT devices were explored. First, methods that attackers could use to communicate with the victim were examined, such that a ransom note was able to be reliably sent to a victim. Next, the viability of using "bricking" as a method of ransom was evaluated, such that devices could be remotely disabled unless the victim makes a payment to the attacker. Research was then performed to ascertain whether it was possible to remotely gain persistence on IoT devices, which would improve the efficacy of existing ransomware methods, and provide opportunities for more advanced ransomware to be created. Finally, after successfully identifying a number of persistence techniques, the viability of privacy-invasion based ransomware was analysed. For each assessed technique, proofs of concept were developed. A range of devices -- with various intended purposes, such as routers, cameras and phones -- were used to test the viability of these proofs of concept. To test communication hijacking, devices' "channels of communication" -- such as web services and embedded screens -- were identified, then hijacked to display custom ransom notes. During the analysis of bricking-based ransomware, a working proof of concept was created, which was then able to remotely brick five IoT devices. After analysing the storage design of an assortment of IoT devices, six different persistence techniques were identified, which were then successfully tested on four devices, such that malicious filesystem modifications would be retained after the device was rebooted. When researching privacy-invasion based ransomware, several methods were created to extract information from data sources that can be commonly found on IoT devices, such as nearby WiFi signals, images from cameras, or audio from microphones. These were successfully implemented in a test environment such that ransomable data could be extracted, processed, and stored for later use to blackmail the victim. Overall, IoT-based ransomware has not only been shown to be viable but also highly damaging to both IoT devices and their users. While the use of IoT-ransomware is still very uncommon "in the wild", the techniques demonstrated within this work highlight an urgent need to improve the security of IoT devices to avoid the risk of IoT-based ransomware causing havoc in our society. Finally, during the development of these proofs of concept, a number of potential countermeasures were identified, which can be used to limit the effectiveness of the attacking techniques discovered in this PhD research

    A Design Science Research Approach to Smart and Collaborative Urban Supply Networks

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    Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness. A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense. Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice

    Anuário científico da Escola Superior de Tecnologia da Saúde de Lisboa - 2021

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    É com grande prazer que apresentamos a mais recente edição (a 11.ª) do Anuário Científico da Escola Superior de Tecnologia da Saúde de Lisboa. Como instituição de ensino superior, temos o compromisso de promover e incentivar a pesquisa científica em todas as áreas do conhecimento que contemplam a nossa missão. Esta publicação tem como objetivo divulgar toda a produção científica desenvolvida pelos Professores, Investigadores, Estudantes e Pessoal não Docente da ESTeSL durante 2021. Este Anuário é, assim, o reflexo do trabalho árduo e dedicado da nossa comunidade, que se empenhou na produção de conteúdo científico de elevada qualidade e partilhada com a Sociedade na forma de livros, capítulos de livros, artigos publicados em revistas nacionais e internacionais, resumos de comunicações orais e pósteres, bem como resultado dos trabalhos de 1º e 2º ciclo. Com isto, o conteúdo desta publicação abrange uma ampla variedade de tópicos, desde temas mais fundamentais até estudos de aplicação prática em contextos específicos de Saúde, refletindo desta forma a pluralidade e diversidade de áreas que definem, e tornam única, a ESTeSL. Acreditamos que a investigação e pesquisa científica é um eixo fundamental para o desenvolvimento da sociedade e é por isso que incentivamos os nossos estudantes a envolverem-se em atividades de pesquisa e prática baseada na evidência desde o início dos seus estudos na ESTeSL. Esta publicação é um exemplo do sucesso desses esforços, sendo a maior de sempre, o que faz com que estejamos muito orgulhosos em partilhar os resultados e descobertas dos nossos investigadores com a comunidade científica e o público em geral. Esperamos que este Anuário inspire e motive outros estudantes, profissionais de saúde, professores e outros colaboradores a continuarem a explorar novas ideias e contribuir para o avanço da ciência e da tecnologia no corpo de conhecimento próprio das áreas que compõe a ESTeSL. Agradecemos a todos os envolvidos na produção deste anuário e desejamos uma leitura inspiradora e agradável.info:eu-repo/semantics/publishedVersio

    The developing maternal-infant relationship: a qualitative longitudinal study

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    Aim The study aimed to explore maternal perceptions and the use of knowledge relating to their infant’s mental health over time using qualitative longitudinal research. Background There has been a growing interest in infant mental health over recent years. Much of this interest is directed through the lens of infant determinism, through knowledge regarding neurological development resulting in biological determinism. Research and policy in this field are directed toward individual parenting behaviours, usually focused on the mother. Despite this, there is little attention given to maternal perspectives of infant mental health, indicating that a more innovative approach to methodology is required. Methods This study took a qualitative longitudinal approach, and interviews were undertaken with seven mothers from the third trimester of pregnancy and then throughout the first year of the infant’s life. Interviews were conducted at 34 weeks of pregnancy, and then when the infant was 6 and 12 weeks, 6, 9, and 12 months, alongside the collection of researcher field notes—a total of 41 interviews. Data were analysed by creating case profiles, memos, and summaries, and then cross-comparison of the emerging narratives. A psycho-socially informed approach was taken to the analysis of data. Findings Three interrelated themes emerged from the data: evolving maternal identity, growing a person, and creating a safe space. The theme of evolving maternal identity dominated the other themes of growing a person and creating a safe space in a way that met perceived socio-cultural requirements for mothering and childcare practices. Participants’ personal stories give voice to their perceptions of the developing maternal-infant relationship in the context of their socio-cultural setting, relationships with others, and experiences over time. Conclusions This study adds new knowledge by giving mothers a voice to express how the maternal-infant relationship develops over time. The findings demonstrate how the developing maternal-infant relationship grows in response to their mutual needs as the mother works to create and sustain identities for herself and the infant that will fit within their socio-cultural context and individual situations. Additionally, the findings illustrate the importance of temporal considerations, social networks, and intergenerational relationships to this evolving process. Recommendations for practice, policy, and education are made that reflect the unique relationship between mother and infant and the need to conceptualise this using an ecological approach

    Despliegue de red LPWAN en entorno industrial con movilidad

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    [ES] La tecnología que comenzó a conectar masivamente a las personas hace décadas se ha desarrollado para conectar dispositivos también. La red de conectividad global resultante se denomina el internet de las cosas. Tiene aplicaciones útiles en todos los sectores de la economía y está preparado para liderar la cuarta revolución industrial, que busca la eficiencia a través de la recopilación de datos. Para lograrlo se necesita un número cada vez mayor de dispositivos, que deben ser eficientes energéticamente para permitir que estas redes sean viables tanto económica como ambientalmente. Cubrir grandes espacios con la menor cantidad posible de recursos de hardware también ayuda a reducir los costes de despliegue, y aquí es exactamente donde entran en juego las redes LPWAN (Low-Power Wide-Area Network). El objetivo de este proyecto es crear una herramienta que permita el despliegue rápido y sencillo de una red LPWAN en un entorno industrial en un contexto de movilidad. El autor ha seleccionado la tecnología LPWAN que mejor se adapta al proyecto (LoRaWAN) y una solución basada en ella, ChirpStack. Se ha desarrollado una aplicación web funcional como candidata ideal para ser la herramienta que permita despliegues de movilidad LPWAN. El uso de la aplicación web desarrollada conlleva además una mayor eficiencia de costes, ya que ahorra al usuario múltiples pasos de configuración tediosos antes de activar un nuevo nodo. Esta herramienta también logra una mayor abstracción de la tecnología de comunicaciones que se está implementando, haciéndola accesible a un mercado aún mayor. Un análisis de los resultados obtenidos destaca el éxito en la consecución de dos objetivos secundarios, la reducción del tiempo de activación del dispositivo final y la abstracción de la tecnología adyacente, además de ser una herramienta de movilidad válida para el despliegue industrial de redes LPWAN.[EN] The technology that started massively connecting people decades ago has been developed to begin connecting devices as well. The resulting global connectivity network is called the Internet of Things. It has useful applications in every sector and is set to lead the fourth industrial revolution. Efficiency through data gathering is the goal of an ever-increasing number of devices. Energy efficiency is key to make this network scalable without skyrocketing electrical consumption. Covering big spaces with as few hardware resources as possible also helps at reducing costs. This is exactly where Low-Power Wide-Area Networks come into play. The aim of this project is to create a tool that allows the fast and easy deployment of a LPWAN network in an industrial environment in a mobility context. The author has selected the LPWAN technology that best fits the project (LoRaWAN) and a solution based on it, ChirpStack. A functional web application has been developed as an ideal candidate to be the tool that allows LPWAN mobility deployments. Further cost efficiency is unlocked by the developed web application, which saves the user multiple tedious configuration steps before activating a new end-device. This tool also achieves further abstraction from the technology that is being implementing, making it accessible to an even greater market. An analysis of the results obtained highlights the success in achieving both secondary goals, a reduction in end-device activation time and an abstraction of the telecommunications technology, apart from being a mobility tool for industrial deployment of LPWAN networks.Hernández Álvarez, R. (2022). Despliegue de red LPWAN en entorno industrial con movilidad. Universitat Politècnica de València. http://hdl.handle.net/10251/181897TFG

    DEEP REINFORCEMENT LEARNING AND MODEL PREDICTIVE CONTROL APPROACHES FOR THE SCHEDULED OPERATION OF DOMESTIC REFRIGERATORS

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    Excess capacity of the UK’s national grid is widely quoted to be reducing to around 4% over the coming years as a consequence of increased economic growth (and hence power usage) and reductions in power generation plants. There is concern that short term variations in power demand could lead to serious wide-scale disruption on a national scale. This is therefore spawning greater attention on augmenting traditional generation plants with renewable and localized energy storage technologies, and consideration of improved demand side responses (DSR), where power consumers are incentivized to switch off assets when the grid is under pressure. It is estimated, for instance, that refrigeration/HVAC systems alone could account for ~14% of the total UK energy usage, with refrigeration and water heating/cooling systems, in particular, being able to act as real-time ‘buffer’ technologies that can be demand-managed to accommodate transient demands by being switched-off for short periods without damaging their outputs. Large populations of thermostatically controlled loads (TCLs) hold significant potential for performing ancillary services in power systems since they are well-established and widely distributed around the power network. In the domestic sector, refrigerators and freezers collectively constitute a very large electrical load since they are continuously connected and are present in almost most households. The rapid proliferation of the ‘Internet of Things’ (IoT) now affords the opportunity to monitor and visualise smart buildings appliances performance and specifically, schedule the operation of the widely distributed domestic refrigerator and freezers to collectively improve energy efficiency and reduce peak power consumption on the electrical grid. To accomplish this, this research proposes the real-time estimation of the thermal mass of individual refrigerators in a network using on-line parameter identification, and the co-ordinated (ON-OFF) scheduling of the refrigerator compressors to maintain their respective temperatures within specified hysteresis bands—commensurate with accommodating food safety standards. Custom Model Predictive Control (MPC) schemes and a Machine Learning algorithm (Reinforcement Learning) are researched to realize an appropriate scheduling methodology which is implemented through COTS IoT hardware. Benefits afforded by the proposed schemes are investigated through experimental trials which show that the co-ordinated operation of domestic refrigerators can 1) reduce the peak power consumption as seen from the perspective of the electrical power grid (i.e. peak power shaving), 2) can adaptively control the temperature hysteresis band of individual refrigerators to increase operational efficiency, and 3) contribute to a widely distributed aggregated load shed for Demand Side Response purposes in order to aid grid stability. Comparative studies of measurements from experimental trials show that the co-ordinated scheduling of refrigerators allows energy savings of between 19% and 29% compared to their traditional isolated (non-co-operative) operation. Moreover, by adaptively changing the hysteresis bands of individual fridges in response to changes in thermal behaviour, a further 20% of savings in energy are possible at local refrigerator level, thereby providing benefits to both network supplier and individual consumer

    Industrial Robotics for Advanced Machining

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    This work presents a literature review of the current state of robotic machining with industrial machining robots, primarily those with 6-axis end effectors and serial link (anthropomorphic) construction. Various disadvantages of robotic machining in industry are presented, as well as the methods applied to mitigate them and discussions of their effects. From this review, the methods of dynamic modelling, stability prediction and configuration control are selected for application to the task of optimisation of a robotic machining cell for drilling operations. Matrix Structural Analysis (MSA) and methods developed by Klimchik et al. are used for compliance modelling, stability prediction methods developed by Altintas et al. and machining stability lobe prediction are then applied to a robotic drilling process, as explored by Mousavi et al. This optimisation method is applied using the measured and estimated properties of an ABB IRB 6640 robot and results are presented in comparison with previous experimentation with the physical robot, and analytical stability predictions from the same cutting parameters with Cutpro software. Results are discussed in the concluding chapters, as well as discontinued parts of the project and suggestions for future work

    Industry 4.0: product digital twins for remanufacturing decision-making

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    Currently there is a desire to reduce natural resource consumption and expand circular business principles whilst Industry 4.0 (I4.0) is regarded as the evolutionary and potentially disruptive movement of technology, automation, digitalisation, and data manipulation into the industrial sector. The remanufacturing industry is recognised as being vital to the circular economy (CE) as it extends the in-use life of products, but its synergy with I4.0 has had little attention thus far. This thesis documents the first investigating into I4.0 in remanufacturing for a CE contributing a design and demonstration of a model that optimises remanufacturing planning using data from different instances in a product’s life cycle. The initial aim of this work was to identify the I4.0 technology that would enhance the stability in remanufacturing with a view to reducing resource consumption. As the project progressed it narrowed to focus on the development of a product digital twin (DT) model to support data-driven decision making for operations planning. The model’s architecture was derived using a bottom-up approach where requirements were extracted from the identified complications in production planning and control that differentiate remanufacturing from manufacturing. Simultaneously, the benefits of enabling visibility of an asset’s through-life health were obtained using a DT as the modus operandi. A product simulator and DT prototype was designed to use Internet of Things (IoT) components, a neural network for remaining life estimations and a search algorithm for operational planning optimisation. The DT was iteratively developed using case studies to validate and examine the real opportunities that exist in deploying a business model that harnesses, and commodifies, early life product data for end-of-life processing optimisation. Findings suggest that using intelligent programming networks and algorithms, a DT can enhance decision-making if it has visibility of the product and access to reliable remanufacturing process information, whilst existing IoT components provide rudimentary “smart” capabilities, but their integration is complex, and the durability of the systems over extended product life cycles needs to be further explored
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