7,077 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

    The Artist as Surveillant: The Use of Surveillance Technology in Contemporary Art

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    Artists have long been called observers, voyeurs, and watchers, and with a particular interest in human behavior and society, they frequently use unknowing passersby as their subjects for works. Curators and scholars explored how artists put citizens under surveillance with photography and videography, which dates back to the early 1900s, years before governments deployed surveillance systems. Since the 1980s, artists have explicitly explored surveillance technology and theory to alert viewers to the rise of surveillance. Today, this genre is called artveillance, a term coined by Andrea Mubi Brighenti in 2010 to categorize art that explicitly deals with surveillance. This genre developed parallel to the rise of mass surveillance which created the current-day surveillance state. Since artveillance dominates the contemporary art scene, I was interested in the history of surveillance technology and themes in art. Although that history is brief, there is a wealth of artworks and studies on the topic. This thesis explores artists who use surveillance technology, specifically close-circuit video, in their practice and how this work has changed over time compared to the rise of government surveillance systems. To properly examine the artwork, each artwork’s technological history and broader cultural context is considered, with careful attention to the artists’ intentions. The thesis starts in the 1970s with Bruce Nauman and Peter Campus’s closed-circuit video installations. The artists did not aim to create a surveillance area but wanted to explore the viewer’s identity with their moving image. In Chapter 2, Julia Scher and Lynn Hershman Leeson’s work from the 1980s and early 1990s is discussed. Created when state surveillance was on the rise, the artists’ work used surveillance technology to critique the systems. The third chapter explores surveillance in a post-9/11 state through Jill Magid and Laura Poitras’s work. The artists exploited and exposed government systems to show how the public’s privacy is invaded. Finally, the paper concludes with an investigation into the public’s relationship with video surveillance, which resembles an apathetic acceptance

    AnuĂĄrio cientĂ­fico da Escola Superior de Tecnologia da SaĂșde de Lisboa - 2021

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    É com grande prazer que apresentamos a mais recente edição (a 11.ÂȘ) do AnuĂĄrio CientĂ­fico da Escola Superior de Tecnologia da SaĂșde de Lisboa. Como instituição de ensino superior, temos o compromisso de promover e incentivar a pesquisa cientĂ­fica em todas as ĂĄreas do conhecimento que contemplam a nossa missĂŁo. Esta publicação tem como objetivo divulgar toda a produção cientĂ­fica desenvolvida pelos Professores, Investigadores, Estudantes e Pessoal nĂŁo Docente da ESTeSL durante 2021. Este AnuĂĄrio Ă©, assim, o reflexo do trabalho ĂĄrduo e dedicado da nossa comunidade, que se empenhou na produção de conteĂșdo cientĂ­fico de elevada qualidade e partilhada com a Sociedade na forma de livros, capĂ­tulos de livros, artigos publicados em revistas nacionais e internacionais, resumos de comunicaçÔes orais e pĂłsteres, bem como resultado dos trabalhos de 1Âș e 2Âș ciclo. Com isto, o conteĂșdo desta publicação abrange uma ampla variedade de tĂłpicos, desde temas mais fundamentais atĂ© estudos de aplicação prĂĄtica em contextos especĂ­ficos de SaĂșde, refletindo desta forma a pluralidade e diversidade de ĂĄreas que definem, e tornam Ășnica, a ESTeSL. Acreditamos que a investigação e pesquisa cientĂ­fica Ă© um eixo fundamental para o desenvolvimento da sociedade e Ă© por isso que incentivamos os nossos estudantes a envolverem-se em atividades de pesquisa e prĂĄtica baseada na evidĂȘncia desde o inĂ­cio dos seus estudos na ESTeSL. Esta publicação Ă© 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 cientĂ­fica e o pĂșblico em geral. Esperamos que este AnuĂĄrio inspire e motive outros estudantes, profissionais de saĂșde, professores e outros colaboradores a continuarem a explorar novas ideias e contribuir para o avanço da ciĂȘncia e da tecnologia no corpo de conhecimento prĂłprio das ĂĄreas que compĂ”e a ESTeSL. Agradecemos a todos os envolvidos na produção deste anuĂĄrio e desejamos uma leitura inspiradora e agradĂĄvel.info:eu-repo/semantics/publishedVersio

    Deep Transfer Learning Applications in Intrusion Detection Systems: A Comprehensive Review

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    Globally, the external Internet is increasingly being connected to the contemporary industrial control system. As a result, there is an immediate need to protect the network from several threats. The key infrastructure of industrial activity may be protected from harm by using an intrusion detection system (IDS), a preventive measure mechanism, to recognize new kinds of dangerous threats and hostile activities. The most recent artificial intelligence (AI) techniques used to create IDS in many kinds of industrial control networks are examined in this study, with a particular emphasis on IDS-based deep transfer learning (DTL). This latter can be seen as a type of information fusion that merge, and/or adapt knowledge from multiple domains to enhance the performance of the target task, particularly when the labeled data in the target domain is scarce. Publications issued after 2015 were taken into account. These selected publications were divided into three categories: DTL-only and IDS-only are involved in the introduction and background, and DTL-based IDS papers are involved in the core papers of this review. Researchers will be able to have a better grasp of the current state of DTL approaches used in IDS in many different types of networks by reading this review paper. Other useful information, such as the datasets used, the sort of DTL employed, the pre-trained network, IDS techniques, the evaluation metrics including accuracy/F-score and false alarm rate (FAR), and the improvement gained, were also covered. The algorithms, and methods used in several studies, or illustrate deeply and clearly the principle in any DTL-based IDS subcategory are presented to the reader

    A Decision Support System for Economic Viability and Environmental Impact Assessment of Vertical Farms

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    Vertical farming (VF) is the practice of growing crops or animals using the vertical dimension via multi-tier racks or vertically inclined surfaces. In this thesis, I focus on the emerging industry of plant-specific VF. Vertical plant farming (VPF) is a promising and relatively novel practice that can be conducted in buildings with environmental control and artificial lighting. However, the nascent sector has experienced challenges in economic viability, standardisation, and environmental sustainability. Practitioners and academics call for a comprehensive financial analysis of VPF, but efforts are stifled by a lack of valid and available data. A review of economic estimation and horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. This thesis proposes an open-source DSS framework to evaluate business sustainability through financial risk and environmental impact assessments. Data from the literature, alongside lessons learned from industry practitioners, would be centralised in the proposed DSS using imprecise data techniques. These techniques have been applied in engineering but are seldom used in financial forecasting. This could benefit complex sectors which only have scarce data to predict business viability. To begin the execution of the DSS framework, VPF practitioners were interviewed using a mixed-methods approach. Learnings from over 19 shuttered and operational VPF projects provide insights into the barriers inhibiting scalability and identifying risks to form a risk taxonomy. Labour was the most commonly reported top challenge. Therefore, research was conducted to explore lean principles to improve productivity. A probabilistic model representing a spectrum of variables and their associated uncertainty was built according to the DSS framework to evaluate the financial risk for VF projects. This enabled flexible computation without precise production or financial data to improve economic estimation accuracy. The model assessed two VPF cases (one in the UK and another in Japan), demonstrating the first risk and uncertainty quantification of VPF business models in the literature. The results highlighted measures to improve economic viability and the viability of the UK and Japan case. The environmental impact assessment model was developed, allowing VPF operators to evaluate their carbon footprint compared to traditional agriculture using life-cycle assessment. I explore strategies for net-zero carbon production through sensitivity analysis. Renewable energies, especially solar, geothermal, and tidal power, show promise for reducing the carbon emissions of indoor VPF. Results show that renewably-powered VPF can reduce carbon emissions compared to field-based agriculture when considering the land-use change. The drivers for DSS adoption have been researched, showing a pathway of compliance and design thinking to overcome the ‘problem of implementation’ and enable commercialisation. Further work is suggested to standardise VF equipment, collect benchmarking data, and characterise risks. This work will reduce risk and uncertainty and accelerate the sector’s emergence

    Image classification over unknown and anomalous domains

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    A longstanding goal in computer vision research is to develop methods that are simultaneously applicable to a broad range of prediction problems. In contrast to this, models often perform best when they are specialized to some task or data type. This thesis investigates the challenges of learning models that generalize well over multiple unknown or anomalous modes and domains in data, and presents new solutions for learning robustly in this setting. Initial investigations focus on normalization for distributions that contain multiple sources (e.g. images in different styles like cartoons or photos). Experiments demonstrate the extent to which existing modules, batch normalization in particular, struggle with such heterogeneous data, and a new solution is proposed that can better handle data from multiple visual modes, using differing sample statistics for each. While ideas to counter the overspecialization of models have been formulated in sub-disciplines of transfer learning, e.g. multi-domain and multi-task learning, these usually rely on the existence of meta information, such as task or domain labels. Relaxing this assumption gives rise to a new transfer learning setting, called latent domain learning in this thesis, in which training and inference are carried out over data from multiple visual domains, without domain-level annotations. Customized solutions are required for this, as the performance of standard models degrades: a new data augmentation technique that interpolates between latent domains in an unsupervised way is presented, alongside a dedicated module that sparsely accounts for hidden domains in data, without requiring domain labels to do so. In addition, the thesis studies the problem of classifying previously unseen or anomalous modes in data, a fundamental problem in one-class learning, and anomaly detection in particular. While recent ideas have been focused on developing self-supervised solutions for the one-class setting, in this thesis new methods based on transfer learning are formulated. Extensive experimental evidence demonstrates that a transfer-based perspective benefits new problems that have recently been proposed in anomaly detection literature, in particular challenging semantic detection tasks

    The Adirondack Chronology

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    The Adirondack Chronology is intended to be a useful resource for researchers and others interested in the Adirondacks and Adirondack history.https://digitalworks.union.edu/arlpublications/1000/thumbnail.jp

    Upgrading Urban Services Through BPL: Practical Applications for Smart Cities

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    Current initiatives related to smart cities in LATAM reveal an increasing interest in the improvement of cities and the wellbeing of their citizens. In addition, specific working groups have been created for this purpose. In this sense, the communication technologies set the basis for gathering, transporting, and managing the large amount of data generated in cities to provide a wide range of services. Within the many alternatives available, BPL positions as a promising technology, since smart cities can greatly benefit of its higher data rates and low latency. In addition, since the medium is already deployed and most of the assets and sensors are connected to the same medium, the cost of the communication systems will be reduced in price and simplicity. The work presents four practical applications: smart buildings, urban lighting, energy assets management and broadband access, in which the possibilities and advantages of BPL are further addressed. Finally, some conclusions and key aspects relating BPL to the success of smart cities are identified.Eusko Jaurlaritza IT-1234-19, KK-202

    Evidence gathering in support of sustainable Scottish inshore fisheries: work package (4) final report: a pilot study to define the footprint and activities of Scottish inshore fisheries by identifying target fisheries, habitats and associated fish stocks

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    [Extract from Executive Summary] This work was conducted under Work package 4 of the European Fisheries Funded program “Evidence Gathering in Support of Sustainable Scottish Inshore Fisheries”. The overall aim of the program was to work in partnership with Marine Scotland Fisheries Policy and with the Scottish Inshore Fisheries Groups to help develop inshore fisheries management. Specifically the program aims were to establish the location of fishing activities within inshore areas; to identify catch composition and associated fishery impacts; to define the environmental footprint and availability of stocks; to develop economic value within local fisheries and; to establish an information resource base to assist the development of inshore fisheries management provisions.Publisher PD
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