We present a novel anomaly-based detection approach capable of detecting botnet Command and Control traffic in an enterprise network by estimating the trustworthiness of the traffic destinations. A traffic flow is classified as anomalous if its destination identifier does not origin from: human input, prior traffic from a trusted destination, or a defined set of legitimate applications. This allows for real-time detection of diverse types of Command and Control traffic. The detection approach and its accuracy are evaluated by experiments in a controlled environment.
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In the Netherlands an innovative programme for early detection of chronic obstructive pulmonary disease (COPD) in primary care among patients aged 40–70 years has been evaluated in both an effect study and a pilot implementation study. Health-care providers identified four obstacles for successful implementation of a COPD early detection programme. This Brief Communication describes the most important results of a qualitative study using in-depth interviews.
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Current symptom detection methods for energy diagnosis in heating, ventilation and air conditioning (HVAC) systems are not standardised and not consistent with HVAC process and instrumentation diagrams (P&IDs) as used by engineers to design and operate these systems, leading to a very limited application of energy performance diagnosis systems in practice. This paper proposes detection methods to overcome these issues, based on the 4S3F (four types of symptom and three types of faults) framework. A set of generic symptoms divided into three categories (balance, energy performance and operational state symptoms) is discussed and related performance indicators are developed, using efficiencies, seasonal performance factors, capacities, and control and design-based operational indicators. The symptom detection method was applied successfully to the HVAC system of the building of The Hague University of Applied Sciences. Detection results on an annual, monthly and daily basis are discussed and compared. Link to the formail publication via its DOI https://doi.org/10.1016/j.autcon.2020.103344
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Mondkapjes, of mondmaskers, zijn door de SARS-COV-2 pandemie niet meer uit het straatbeeld weg te denken. De kwaliteit en comfort van de pasvorm van medische en niet-medische mondmaskers wordt bepaald door hoe goed het mondmasker overeenkomt met de afmetingen van het gezicht van de drager. Echter is er geen goed overzicht van de antropometrie van het gelaat van de Nederlandse bevolking waardoor de pasvorm van mondmaskers nu vaak niet optimaal is. Er is dus vraag naar een laagdrempelige en veilige manier om gezichtskenmerken in kaart te brengen en betere ontwerprichtlijnen voor mondkapjes. Driedimensionaal (3D) scannen doormiddel van Light Detection and Ranging (LiDaR) technologie in combinatie met slimme algoritmes lijkt wellicht een manier om gezichtskenmerken snel en laagdrempelig vast te leggen bij grote groepen mensen. Daarnaast geeft het 3D scannen van gezichten de mogelijkheid om niet enkel de afmetingen van gezichten te meten, maar ook 3D pasvisualisaties uit te voeren. Hoewel 3D scannen geen nieuwe technologie is, is de LiDaR technologie pas sinds 2020 geïntegreerd in de Ipad en Iphone waardoor het toegankelijk gemaakt is voor consumenten. Doormiddel van een research through design benadering zal onderzocht worden of deze technologie gebruikt kan worden om betrouwbare en valide opnames te maken van gezichten en of er op basis hiervan ontwerprichtlijnen ontwikkeld kunnen worden. In dit KIEM GoCi-project zal daarnaast ingezet worden om een kennisbasis en netwerk op te bouwen voor een vervolg aanvraag over de inzet van 3D technologieën in de mode-industrie.
The increasing amount of electronic waste (e-waste) urgently requires the use of innovative solutions within the circular economy models in this industry. Sorting of e-waste in a proper manner are essential for the recovery of valuable materials and minimizing environmental problems. The conventional e-waste sorting models are time-consuming processes, which involve laborious manual classification of complex and diverse electronic components. Moreover, the sector is lacking in skilled labor, thus making automation in sorting procedures is an urgent necessity. The project “AdapSort: Adaptive AI for Sorting E-Waste” aims to develop an adaptable AI-based system for optimal and efficient e-waste sorting. The project combines deep learning object detection algorithms with open-world vision-language models to enable adaptive AI models that incorporate operator feedback as part of a continuous learning process. The project initiates with problem analysis, including use case definition, requirement specification, and collection of labeled image data. AI models will be trained and deployed on edge devices for real-time sorting and scalability. Then, the feasibility of developing adaptive AI models that capture the state-of-the-art open-world vision-language models will be investigated. The human-in-the-loop learning is an important feature of this phase, wherein the user is enabled to provide ongoing feedback about how to refine the model further. An interface will be constructed to enable human intervention to facilitate real-time improvement of classification accuracy and sorting of different items. Finally, the project will deliver a proof of concept for the AI-based sorter, validated through selected use cases in collaboration with industrial partners. By integrating AI with human feedback, this project aims to facilitate e-waste management and serve as a foundation for larger projects.
The utilization of drones in various industries, such as agriculture, infrastructure inspection, and surveillance, has significantly increased in recent years. However, navigating low-altitude environments poses a challenge due to potential collisions with “unseen” obstacles like power lines and poles, leading to safety concerns and equipment damage. Traditional obstacle avoidance systems often struggle with detecting thin and transparent obstacles, making them ill-suited for scenarios involving power lines, which are essential yet difficult to perceive visually. Together with partners that are active in logistics and safety and security domains, this project proposal aims at conducting feasibility study on advanced obstacle detection and avoidance system for low-flying drones. To that end, the main research question is, “How can AI-enabled, robust and module invisible obstacle avoidance technology can be developed for low-flying drones? During this feasibility study, cutting-edge sensor technologies, such as LiDAR, radar, camera and advanced machine learning algorithms will be investigated to what extent they can be used be to accurately detect “Not easily seen” obstacles in real-time. The successful conclusion of this project will lead to a bigger project that aims to contribute to the advancement of drone safety and operational capabilities in low-altitude environments, opening new possibilities for applications in industries where low-flying drones and obstacle avoidance are critical.
