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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Mäkipää, Raisa; Abramoff, Rose; Adamczyk, Bartosz; Baldy, Virginie; +26 Authors

    The global forest carbon (C) stock is estimated at 662 Gt of which 45% is in soil organic matter. Thus, comprehensive understanding of the effects of forest management practices on forest soil C stock and greenhouse gas (GHG) fluxes is needed for the development of effective forest-based climate change mitigation strategies. To improve this understanding, we synthesized peer-reviewed literature on forest management practices that can mitigate climate change by increasing soil C stocks and reducing GHG emissions. We further identified soil processes that affect soil GHG balance and discussed how models represent forest management effects on soil in GHG inventories and scenario analyses to address forest climate change mitigation potential. Forest management effects depend strongly on the specific practice and land type. Intensive timber harvesting with removal of harvest residues/stumps results in a reduction in soil C stock, while high stocking density and enhanced productivity by fertilization or dominance of coniferous species increase soil C stock. Nitrogen fertilization increases the soil C stock and N2O emissions while decreasing the CH4 sink. Peatland hydrology management is a major driver of the GHG emissions of the peatland forests, with lower water level corresponding to higher CO2 emissions. Furthermore, the global warming potential of all GHG emissions (CO2, CH4 and N2O) together can be ten-fold higher after clear-cutting than in peatlands with standing trees. The climate change mitigation potential of forest soils, as estimated by modelling approaches, accounts for stand biomass driven effects and climate factors that affect the decomposition rate. A future challenge is to account for the effects of soil preparation and other management that affects soil processes by changing soil temperature, soil moisture, soil nutrient balance, microbial community structure and processes, hydrology and soil oxygen concentration in the models. We recommend that soil monitoring and modelling focus on linking processes of soil C stabilization with the functioning of soil microbiota. This review has been supported by the grant Holistic management practices, modelling and monitoring for European forest soils – HoliSoils (EU Horizon 2020 Grant Agreement No 101000289) and the Academy of Finland Fellow project (330136, B. Adamczyk). In addition to the HoliSoils consortium partners, Dr. Abramoff contributed on this study and her work was supported by the United States Department of Energy, Office of Science, Office of Biological and Environmental Research. Oak Ridge National Laboratory is managed by UT-Battelle, LLC, for the United States Department of Energy under contract DE-AC05-00OR22725.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ NARCIS; Research@WURarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    NARCIS; Research@WUR
    Article . 2023
    License: CC BY
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    Forest Ecology and Management
    Other literature type . 2023
    Data sources: NARCIS
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Repositori Obert UdL
    Article . 2023
    License: CC BY
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    Forest Ecology and Management; Research@WUR; ZENODO
    Other literature type . Article . 2022 . 2023 . Peer-reviewed
    License: Elsevier TDM
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Forest Ecology and Management
    Article . 2022
    Data sources: JAIRO
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  • Authors: Hemmer, Adrien;

    This thesis concerns new detection methods for the security of heterogenous IoT systems, and fits within the framework of the SecureIoT European project. We have first proposed a solution exploiting the process mining together with pre-treatment techniques, in order to build behavioral models, and identifying anomalies from heterogenous systems. We have then evaluated this solution from datasets coming from different application domains : connected cars, industry 4.0, and assistance robots.. This solution enables to build models that are more easily understandable. It provides better detection results than other common methods, but may generate a longer detection time. In order to reduce this time without degrading detection performances, we have then extended our method with an ensemble approach, which combines the results from several detection methods that are used simultaneously. In particular, we have compared different score aggregation strategies, as well as evaluated a feedback mechanism for dynamically adjusting the sensitivity of the detection. Finally, we have implemented the solution as a prototype, that has been integrated into a security platform developed in collaboration with other European industrial partners.; Cette thèse porte sur de nouvelles méthodes de détection pour la sécurité des systèmes IoT hétérogènes, et s'inscrit dans le cadre du projet européen Secure IoT. Nous avons tout d'abord proposé une solution utilisant le process mining couplé à un pré-traitement des données, pour construire des modèles de comportement et identifier des anomalies à partir de données hétérogènes. Nous avons évalué cette solution à partir de jeux de données issus de plusieurs domaines d'applications différents : véhicules connectés, industrie 4.0, robots d'assistance. Cette solution permet de construire des modèles plus facilement compréhensibles. Elle obtient des meilleurs résultats de détection que d'autres méthodes usuelles, mais demande un temps de traitement plus long. Pour réduire ce dernier sans dégrader les performances de détection, nous avons ensuite étendu notre méthode à l'aide d'une approche ensembliste, qui permet de combiner les résultats de plusieurs méthodes de détection utilisées simultanément. En particulier, nous avons comparé différentes stratégies d'agrégation des scores. Nous avons aussi évalué un mécanisme permettant d'ajuster dynamiquement la sensibilité de la détection. Enfin, nous avons implanté la solution sous la forme d'un prototype, qui a été intégré à une plateforme de sécurité développée avec des partenaires européens.

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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Mélanie Munch; Patrice Buche; Stéphane Dervaux; Juliette Dibie; +4 Authors

    International audience; This paper presents a workflow for the design of transformation processes using different kinds of expert’s knowledge. It introduces POND (Process and observation ONtology Discovery), a workflow dedicated to answer expert’s questions about processes. It addresses two main issues: (1) how to represent the processes inner complexity, and (2) how to reason about processes taking into account uncertainty and causality. First, we show how to use a semantic model, an ontology, and its associated data to answer some of the expert’s questions concerning the processes, using semantic web languages and technologies. Then, we describe how to learn a predictive model, to discover new knowledge and provide explicative models by integrating the semantic model into a probabilistic relational model. The result is a complete workflow able to extensively analyze transformation processes through all their granularity levels and answer expert’s questions about their domains. An example of this workflow is given on biocomposites manufacturing for food packaging.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Expert Systems with ...arrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Expert Systems with Applications
    Article . 2022 . Peer-reviewed
    License: Elsevier TDM
    Data sources: Crossref
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Ferry, Nicolas; Dautov, Rustem; Song, Hui;

    International audience; One of the most prominent implementations of the serverless programming model is Function-as-a-Service (FaaS). Using FaaS, application developers provide source code of serverless functions, typically describing only parts of a larger application, and define triggers for executing these functions on infrastructure components managed by the FaaS provider. There are still challenges that hinder the wider adoption of the FaaS model across the whole Cloud-Edge-IoT continuum. These include the high heterogeneity of the Edge and IoT infrastructure, vendor lock-in, the need to deploy and adapt serverless functions as well as their supporting services and software stacks into their cyber-physical execution environment. As a first step towards addressing these challenges, we introduce the \siot platform for the design, deployment, and maintenance of applications over the Cloud-Edge-IoT continuum. In particular, our platform enables the specification and deployment of serverless functions on Cloud and Edge resources, as well as the deployment of their supporting services and software stacks over the whole Cloud-Edge-IoT continuum.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ SINTEF Open; Norwegi...arrow_drop_down
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    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Gauthier, Raphaël; Largouët, Christine; Dourmad, Jean-Yves;

    International audience; Predicting litter performance in lactating sows is an essential step towards the development of decision support systems for precision feeding in lactating sows. Numerous factors affecting litter performance have been described in literature. However, predictive models working on-farm in real time are not available. The main objectives of this research was to (i) explore 4 different machine learning strategies, and (ii) identify the best supervised learning algorithm in order to obtain reliable predictions of litter performance. This study was carried out with data obtained from 6 experimental farms over the last 20 years. Algorithms were trained to predict the litter weight at weaning using a set of 4 numeric and 3 categorical features, and a method for predicting secondary litter performance and nutrient output in milk from the predicted litter weight at weaning was evaluated. To evaluate the reliability of predictions within each farm, the mean error per farm (MEf) and the mean absolute percentage error per farm (MAPEf) were computed. The best performance for the prediction of litter weight at weaning was obtained with an ensemble algorithm with farm-level training and testing (MEf = −0.14 kg; MAPEf = 9.01%), but performance with simple linear regression was very close (MAPEf = 9.30%). Learning across all farms only achieved comparable results with the neural networks algorithm, but at higher computational costs. The method for predicting secondary litter performance and nutrient output from the predictions of litter weight at weaning reveals that the MEf remains close to 0, and that the MAPEf only increases by a few percentage points. This study confirms the effect of numerous factors known in the literature to affect litter performance, such as litter size and parity of sows, but also revealed huge variations between farms. According to this study, reliable predictions could be obtained with interpretable supervised algorithms trained at farm level, with features that can be easily measured on-farm. This study thus shows that on-farm data are necessary to accurately train models and make reliable predictions at farm level. These predictions could be used by decision support systems in order to develop precision feeding approaches in lactating sows.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Computers and Electr...arrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Computers and Electronics in Agriculture
    Article . 2022 . Peer-reviewed
    License: Elsevier TDM
    Data sources: Crossref
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Munch, Mélanie; Buche, Patrice; Dervaux, Stéphane; Breysse, Amélie; +6 Authors

    International audience; Due to the rising amount of plastic waste generated each year, multiple questions are emerging about their harmful long-term effects on the environment, the ecosystems and human health. One possible strategy to mitigate these issues is to substitute conventional plastics by materials fully biodegradable in natural conditions, such as poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV). In order to decrease the overall cost and environmental impact of PHBV-based materials while modulating their technical performance, PHBV can be combined with lignocellulosic fillers. In this article, a total of 88 formulations of PHBV-based biocomposites has been collected, distributed over 5 interdisciplinary projects involving computer scientists, data scientists and biomass processing experts for food and bio-based material production. Available data concern the technical process descriptions, including the description of each step and the different observations measured. These data are stored in a knowledge base that can be queried on the Web.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Agritroparrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Data in Brief
    Article . 2022
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    https://doi.org/10.1016/j.dib....
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    HAL Descartes
    Article . 2022
    License: CC BY
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    Article . 2022
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Agritroparrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Data in Brief
      Article . 2022
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      https://doi.org/10.1016/j.dib....
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Tanusan Rajmohan; Phu H. Nguyen; Nicolas Ferry;

    International audience; Abstract Security of the Internet of Things (IoT)-based Smart Systems involving sensors, actuators and distributed control loop is of paramount importance but very difficult to address. Security patterns consist of domain-independent time-proven security knowledge and expertise. How are they useful for developing secure IoT-based smart systems? Are there architectures that support IoT security? We aim to systematically review the research work published on patterns and architectures for IoT security (and privacy). Then, we want to provide an analysis on that research landscape to answer our research questions. We follow the well-known guidelines for conducting systematic literature reviews. From thousands of candidate papers initially found in our search process, we have systematically distinguished and analyzed thirty-six (36) papers that have been peer-reviewed and published around patterns and architectures for IoT security and privacy in the last decade (January 2010–December 2020). Our analysis shows that there is a rise in the number of publications tending to patterns and architectures for IoT security in the last three years. We have not seen any approach of applying systematically architectures and patterns together that can address security (and privacy) concerns not only at the architectural level, but also at the network or IoT devices level. We also explored how the research contributions in the primary studies handle the different issues from the OWASP Internet of Things (IoT) top ten vulnerabilities list. Finally, we discuss the current gaps in this research area and how to fill in the gaps for promoting the utilization of patterns for IoT security and privacy by design.Security of the Internet of Things (IoT)-based Smart Systems involving sensors, actuators and distributed control loop is of paramount importance but very difficult to address. Security patterns consist of domain-independent time-proven security knowledge and expertise. How are they useful for developing secure IoT-based smart systems? Are there architectures that support IoT security? We aim to systematically review the research work published on patterns and architectures for IoT security (and privacy). Then, we want to provide an analysis on that research landscape to answer our research questions. We follow the well-known guidelines for conducting systematic literature reviews. From thousands of candidate papers initially found in our search process, we have systematically distinguished and analyzed thirty-six (36) papers that have been peer-reviewed and published around patterns and architectures for IoT security and privacy in the last decade (January 2010–December 2020). Our analysis shows that there is a rise in the number of publications tending to patterns and architectures for IoT security in the last three years. We have not seen any approach of applying systematically architectures and patterns together that can address security (and privacy) concerns not only at the architectural level, but also at the network or IoT devices level. We also explored how the research contributions in the primary studies handle the different issues from the OWASP Internet of Things (IoT) top ten vulnerabilities list. Finally, we discuss the current gaps in this research area and how to fill in the gaps for promoting the utilization of patterns for IoT security and privacy by design.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Cybersecurityarrow_drop_down
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    Cybersecurity
    Article . 2022 . Peer-reviewed
    License: CC BY
    Data sources: Sygma; Crossref
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    SINTEF Open
    Article . 2022
    Data sources: SINTEF Open
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    Cybersecurity
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    ZENODO
    Article . 2022
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Article . 2022
    License: CC BY
    Data sources: Datacite
    Hal-Diderot
    Article . 2022
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    Authors: Hemmer, Adrien; Abderrahim, Mohamed; Badonnel, Remi; Chrisment, Isabelle;

    International audience; The Internet of Things has known an important development. However, security management is still a key challenge in particular for deploying complex IoT systems that provide sophisticated services. In this paper, we design an ensemble learning-based architecture to support early security detection in the context of multi-step attacks, by leveraging the performance of different detection techniques. The architecture relies on a total of five major methods, including process mining, elliptic envelope, one class support vector machine, local outlier factor and isolation forest. We describe the main components of this architecture and their interactions, from the data preprocessing to the generation of alerts, through the calculation of scores. The different detection methods are executed in parallel, and their results are combined by an ensemble learning strategy in order to improve the overall detection performance. We develop a proof-of-concept prototype and perform a large set of experiments to quantify the benefits and limits of this approach based on industrial datasets.

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    https://doi.org/10.23919/cnsm5...
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    Authors: Junges, Pierre-Marie; François, Jérome; Festor, Olivier;

    International audience; Internet-of-Things (IoT) devices or more generally embedded devices are nowadays commonly deployed in public, personal or work spaces despite suffering from security issues often related to their bad design and/or configuration. For instance, IoT botnets such as Mirai successfully compromised thousands of devices using a bruteforce method on a set of known credentials. Although brute-force attacks against a particular service (e.g. SSH, telnet) generate many packets which can be easily detected and mitigated, attackers can easily rely on TCP scans to assess the services present on a device while maintaining a high level of stealthiness. In this paper, we present a method to reconstruct precise information about an IoT device configuration (brand name, usernames, passwords, software components) from partial knowledge such as open ports revealed by a TCP scan. It relies on constituting a knowledge base from a large dataset of publicly accessible firmware serving as training multiple Random Forest (RF) classifiers. Using a dataset of 6935 embedded devices, the HTTP, SSH or DNS software names can be predicted with a precision higher than 80% with a limited knowledge. The correct HTTP, SSH or DNS versions can be inferred in more than 95% of cases after 1.4 trials on average. Similarly, our technique also predicts the password of at least one valid user in more than 97% of the cases after 1.15 trials on average.

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    https://doi.org/10.23919/cnsm5...
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    Authors: Claeys, Timothy; Vucinic, Malisa; Watteyne, Thomas; Rousseau, Franck; +1 Authors

    This paper presents a thorough comparison of the Transport Layer Security (TLS) v1.2 and Datagram TLS (DTLS) v1.2 handshake in 6TiSCH networks. TLS and DTLS play a crucial role in protecting daily Internet traffic, while 6TiSCH is a major low-power link layer technology for the IoT. In recent years, DTLS has been the de-facto security protocol to protect IoT application traffic, mainly because it runs over lightweight, unreliable transport protocols, i.e., UDP. However, unlike the DTLS record layer, the handshake requires reliable message delivery. It, therefore, incorporates sequence numbers, a retransmission timer, and a fragmentation algorithm. Our goal is to study how well these mechanisms perform, in the constrained setting of 6TiSCH, compared to TCP’s reliability algorithms, relied upon by TLS. We port the mbedTLS library to OpenWSN, a 6TiSCH reference implementation, and deploy the code on the state-of-the-art OpenMote platform. We show that, when the peers use an ideal channel, the DTLS handshake uses up to 800 less and completes 0.6 s faster. Nonetheless, using an unreliable communication link, the DTLS handshake duration suffers a performance penalty of roughly 45%, while TLS’ handshake duration degrades by merely 15%. Similarly, the number of exchanged bytes doubles for DTLS while for TLS the increase is limited to 15%. The results indicate that IoT product developers should account for network characteristics when selecting a security protocol. Neglecting to do so can negatively impact the battery lifetime of the entire constrained network.

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    Authors: Mäkipää, Raisa; Abramoff, Rose; Adamczyk, Bartosz; Baldy, Virginie; +26 Authors

    The global forest carbon (C) stock is estimated at 662 Gt of which 45% is in soil organic matter. Thus, comprehensive understanding of the effects of forest management practices on forest soil C stock and greenhouse gas (GHG) fluxes is needed for the development of effective forest-based climate change mitigation strategies. To improve this understanding, we synthesized peer-reviewed literature on forest management practices that can mitigate climate change by increasing soil C stocks and reducing GHG emissions. We further identified soil processes that affect soil GHG balance and discussed how models represent forest management effects on soil in GHG inventories and scenario analyses to address forest climate change mitigation potential. Forest management effects depend strongly on the specific practice and land type. Intensive timber harvesting with removal of harvest residues/stumps results in a reduction in soil C stock, while high stocking density and enhanced productivity by fertilization or dominance of coniferous species increase soil C stock. Nitrogen fertilization increases the soil C stock and N2O emissions while decreasing the CH4 sink. Peatland hydrology management is a major driver of the GHG emissions of the peatland forests, with lower water level corresponding to higher CO2 emissions. Furthermore, the global warming potential of all GHG emissions (CO2, CH4 and N2O) together can be ten-fold higher after clear-cutting than in peatlands with standing trees. The climate change mitigation potential of forest soils, as estimated by modelling approaches, accounts for stand biomass driven effects and climate factors that affect the decomposition rate. A future challenge is to account for the effects of soil preparation and other management that affects soil processes by changing soil temperature, soil moisture, soil nutrient balance, microbial community structure and processes, hydrology and soil oxygen concentration in the models. We recommend that soil monitoring and modelling focus on linking processes of soil C stabilization with the functioning of soil microbiota. This review has been supported by the grant Holistic management practices, modelling and monitoring for European forest soils – HoliSoils (EU Horizon 2020 Grant Agreement No 101000289) and the Academy of Finland Fellow project (330136, B. Adamczyk). In addition to the HoliSoils consortium partners, Dr. Abramoff contributed on this study and her work was supported by the United States Department of Energy, Office of Science, Office of Biological and Environmental Research. Oak Ridge National Laboratory is managed by UT-Battelle, LLC, for the United States Department of Energy under contract DE-AC05-00OR22725.

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    Article . 2023
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    Article . 2023
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    Forest Ecology and Management; Research@WUR; ZENODO
    Other literature type . Article . 2022 . 2023 . Peer-reviewed
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  • Authors: Hemmer, Adrien;

    This thesis concerns new detection methods for the security of heterogenous IoT systems, and fits within the framework of the SecureIoT European project. We have first proposed a solution exploiting the process mining together with pre-treatment techniques, in order to build behavioral models, and identifying anomalies from heterogenous systems. We have then evaluated this solution from datasets coming from different application domains : connected cars, industry 4.0, and assistance robots.. This solution enables to build models that are more easily understandable. It provides better detection results than other common methods, but may generate a longer detection time. In order to reduce this time without degrading detection performances, we have then extended our method with an ensemble approach, which combines the results from several detection methods that are used simultaneously. In particular, we have compared different score aggregation strategies, as well as evaluated a feedback mechanism for dynamically adjusting the sensitivity of the detection. Finally, we have implemented the solution as a prototype, that has been integrated into a security platform developed in collaboration with other European industrial partners.; Cette thèse porte sur de nouvelles méthodes de détection pour la sécurité des systèmes IoT hétérogènes, et s'inscrit dans le cadre du projet européen Secure IoT. Nous avons tout d'abord proposé une solution utilisant le process mining couplé à un pré-traitement des données, pour construire des modèles de comportement et identifier des anomalies à partir de données hétérogènes. Nous avons évalué cette solution à partir de jeux de données issus de plusieurs domaines d'applications différents : véhicules connectés, industrie 4.0, robots d'assistance. Cette solution permet de construire des modèles plus facilement compréhensibles. Elle obtient des meilleurs résultats de détection que d'autres méthodes usuelles, mais demande un temps de traitement plus long. Pour réduire ce dernier sans dégrader les performances de détection, nous avons ensuite étendu notre méthode à l'aide d'une approche ensembliste, qui permet de combiner les résultats de plusieurs méthodes de détection utilisées simultanément. En particulier, nous avons comparé différentes stratégies d'agrégation des scores. Nous avons aussi évalué un mécanisme permettant d'ajuster dynamiquement la sensibilité de la détection. Enfin, nous avons implanté la solution sous la forme d'un prototype, qui a été intégré à une plateforme de sécurité développée avec des partenaires européens.

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    Authors: Mélanie Munch; Patrice Buche; Stéphane Dervaux; Juliette Dibie; +4 Authors

    International audience; This paper presents a workflow for the design of transformation processes using different kinds of expert’s knowledge. It introduces POND (Process and observation ONtology Discovery), a workflow dedicated to answer expert’s questions about processes. It addresses two main issues: (1) how to represent the processes inner complexity, and (2) how to reason about processes taking into account uncertainty and causality. First, we show how to use a semantic model, an ontology, and its associated data to answer some of the expert’s questions concerning the processes, using semantic web languages and technologies. Then, we describe how to learn a predictive model, to discover new knowledge and provide explicative models by integrating the semantic model into a probabilistic relational model. The result is a complete workflow able to extensively analyze transformation processes through all their granularity levels and answer expert’s questions about their domains. An example of this workflow is given on biocomposites manufacturing for food packaging.

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    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Expert Systems with Applications
    Article . 2022 . Peer-reviewed
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    Authors: Ferry, Nicolas; Dautov, Rustem; Song, Hui;

    International audience; One of the most prominent implementations of the serverless programming model is Function-as-a-Service (FaaS). Using FaaS, application developers provide source code of serverless functions, typically describing only parts of a larger application, and define triggers for executing these functions on infrastructure components managed by the FaaS provider. There are still challenges that hinder the wider adoption of the FaaS model across the whole Cloud-Edge-IoT continuum. These include the high heterogeneity of the Edge and IoT infrastructure, vendor lock-in, the need to deploy and adapt serverless functions as well as their supporting services and software stacks into their cyber-physical execution environment. As a first step towards addressing these challenges, we introduce the \siot platform for the design, deployment, and maintenance of applications over the Cloud-Edge-IoT continuum. In particular, our platform enables the specification and deployment of serverless functions on Cloud and Edge resources, as well as the deployment of their supporting services and software stacks over the whole Cloud-Edge-IoT continuum.

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    Authors: Gauthier, Raphaël; Largouët, Christine; Dourmad, Jean-Yves;

    International audience; Predicting litter performance in lactating sows is an essential step towards the development of decision support systems for precision feeding in lactating sows. Numerous factors affecting litter performance have been described in literature. However, predictive models working on-farm in real time are not available. The main objectives of this research was to (i) explore 4 different machine learning strategies, and (ii) identify the best supervised learning algorithm in order to obtain reliable predictions of litter performance. This study was carried out with data obtained from 6 experimental farms over the last 20 years. Algorithms were trained to predict the litter weight at weaning using a set of 4 numeric and 3 categorical features, and a method for predicting secondary litter performance and nutrient output in milk from the predicted litter weight at weaning was evaluated. To evaluate the reliability of predictions within each farm, the mean error per farm (MEf) and the mean absolute percentage error per farm (MAPEf) were computed. The best performance for the prediction of litter weight at weaning was obtained with an ensemble algorithm with farm-level training and testing (MEf = −0.14 kg; MAPEf = 9.01%), but performance with simple linear regression was very close (MAPEf = 9.30%). Learning across all farms only achieved comparable results with the neural networks algorithm, but at higher computational costs. The method for predicting secondary litter performance and nutrient output from the predictions of litter weight at weaning reveals that the MEf remains close to 0, and that the MAPEf only increases by a few percentage points. This study confirms the effect of numerous factors known in the literature to affect litter performance, such as litter size and parity of sows, but also revealed huge variations between farms. According to this study, reliable predictions could be obtained with interpretable supervised algorithms trained at farm level, with features that can be easily measured on-farm. This study thus shows that on-farm data are necessary to accurately train models and make reliable predictions at farm level. These predictions could be used by decision support systems in order to develop precision feeding approaches in lactating sows.

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    Computers and Electronics in Agriculture
    Article . 2022 . Peer-reviewed
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    Authors: Munch, Mélanie; Buche, Patrice; Dervaux, Stéphane; Breysse, Amélie; +6 Authors

    International audience; Due to the rising amount of plastic waste generated each year, multiple questions are emerging about their harmful long-term effects on the environment, the ecosystems and human health. One possible strategy to mitigate these issues is to substitute conventional plastics by materials fully biodegradable in natural conditions, such as poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV). In order to decrease the overall cost and environmental impact of PHBV-based materials while modulating their technical performance, PHBV can be combined with lignocellulosic fillers. In this article, a total of 88 formulations of PHBV-based biocomposites has been collected, distributed over 5 interdisciplinary projects involving computer scientists, data scientists and biomass processing experts for food and bio-based material production. Available data concern the technical process descriptions, including the description of each step and the different observations measured. These data are stored in a knowledge base that can be queried on the Web.

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    Data in Brief
    Article . 2022
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    https://doi.org/10.1016/j.dib....
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      Data in Brief
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      https://doi.org/10.1016/j.dib....
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    Authors: Tanusan Rajmohan; Phu H. Nguyen; Nicolas Ferry;

    International audience; Abstract Security of the Internet of Things (IoT)-based Smart Systems involving sensors, actuators and distributed control loop is of paramount importance but very difficult to address. Security patterns consist of domain-independent time-proven security knowledge and expertise. How are they useful for developing secure IoT-based smart systems? Are there architectures that support IoT security? We aim to systematically review the research work published on patterns and architectures for IoT security (and privacy). Then, we want to provide an analysis on that research landscape to answer our research questions. We follow the well-known guidelines for conducting systematic literature reviews. From thousands of candidate papers initially found in our search process, we have systematically distinguished and analyzed thirty-six (36) papers that have been peer-reviewed and published around patterns and architectures for IoT security and privacy in the last decade (January 2010–December 2020). Our analysis shows that there is a rise in the number of publications tending to patterns and architectures for IoT security in the last three years. We have not seen any approach of applying systematically architectures and patterns together that can address security (and privacy) concerns not only at the architectural level, but also at the network or IoT devices level. We also explored how the research contributions in the primary studies handle the different issues from the OWASP Internet of Things (IoT) top ten vulnerabilities list. Finally, we discuss the current gaps in this research area and how to fill in the gaps for promoting the utilization of patterns for IoT security and privacy by design.Security of the Internet of Things (IoT)-based Smart Systems involving sensors, actuators and distributed control loop is of paramount importance but very difficult to address. Security patterns consist of domain-independent time-proven security knowledge and expertise. How are they useful for developing secure IoT-based smart systems? Are there architectures that support IoT security? We aim to systematically review the research work published on patterns and architectures for IoT security (and privacy). Then, we want to provide an analysis on that research landscape to answer our research questions. We follow the well-known guidelines for conducting systematic literature reviews. From thousands of candidate papers initially found in our search process, we have systematically distinguished and analyzed thirty-six (36) papers that have been peer-reviewed and published around patterns and architectures for IoT security and privacy in the last decade (January 2010–December 2020). Our analysis shows that there is a rise in the number of publications tending to patterns and architectures for IoT security in the last three years. We have not seen any approach of applying systematically architectures and patterns together that can address security (and privacy) concerns not only at the architectural level, but also at the network or IoT devices level. We also explored how the research contributions in the primary studies handle the different issues from the OWASP Internet of Things (IoT) top ten vulnerabilities list. Finally, we discuss the current gaps in this research area and how to fill in the gaps for promoting the utilization of patterns for IoT security and privacy by design.

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    Cybersecurity
    Article . 2022 . Peer-reviewed
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    Authors: Hemmer, Adrien; Abderrahim, Mohamed; Badonnel, Remi; Chrisment, Isabelle;

    International audience; The Internet of Things has known an important development. However, security management is still a key challenge in particular for deploying complex IoT systems that provide sophisticated services. In this paper, we design an ensemble learning-based architecture to support early security detection in the context of multi-step attacks, by leveraging the performance of different detection techniques. The architecture relies on a total of five major methods, including process mining, elliptic envelope, one class support vector machine, local outlier factor and isolation forest. We describe the main components of this architecture and their interactions, from the data preprocessing to the generation of alerts, through the calculation of scores. The different detection methods are executed in parallel, and their results are combined by an ensemble learning strategy in order to improve the overall detection performance. We develop a proof-of-concept prototype and perform a large set of experiments to quantify the benefits and limits of this approach based on industrial datasets.

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    https://doi.org/10.23919/cnsm5...
    Conference object . 2021 . Peer-reviewed
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      https://doi.org/10.23919/cnsm5...
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    Authors: Junges, Pierre-Marie; François, Jérome; Festor, Olivier;

    International audience; Internet-of-Things (IoT) devices or more generally embedded devices are nowadays commonly deployed in public, personal or work spaces despite suffering from security issues often related to their bad design and/or configuration. For instance, IoT botnets such as Mirai successfully compromised thousands of devices using a bruteforce method on a set of known credentials. Although brute-force attacks against a particular service (e.g. SSH, telnet) generate many packets which can be easily detected and mitigated, attackers can easily rely on TCP scans to assess the services present on a device while maintaining a high level of stealthiness. In this paper, we present a method to reconstruct precise information about an IoT device configuration (brand name, usernames, passwords, software components) from partial knowledge such as open ports revealed by a TCP scan. It relies on constituting a knowledge base from a large dataset of publicly accessible firmware serving as training multiple Random Forest (RF) classifiers. Using a dataset of 6935 embedded devices, the HTTP, SSH or DNS software names can be predicted with a precision higher than 80% with a limited knowledge. The correct HTTP, SSH or DNS versions can be inferred in more than 95% of cases after 1.4 trials on average. Similarly, our technique also predicts the password of at least one valid user in more than 97% of the cases after 1.15 trials on average.

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    https://doi.org/10.23919/cnsm5...
    Conference object . 2021 . Peer-reviewed
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      https://doi.org/10.23919/cnsm5...
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Claeys, Timothy; Vucinic, Malisa; Watteyne, Thomas; Rousseau, Franck; +1 Authors

    This paper presents a thorough comparison of the Transport Layer Security (TLS) v1.2 and Datagram TLS (DTLS) v1.2 handshake in 6TiSCH networks. TLS and DTLS play a crucial role in protecting daily Internet traffic, while 6TiSCH is a major low-power link layer technology for the IoT. In recent years, DTLS has been the de-facto security protocol to protect IoT application traffic, mainly because it runs over lightweight, unreliable transport protocols, i.e., UDP. However, unlike the DTLS record layer, the handshake requires reliable message delivery. It, therefore, incorporates sequence numbers, a retransmission timer, and a fragmentation algorithm. Our goal is to study how well these mechanisms perform, in the constrained setting of 6TiSCH, compared to TCP’s reliability algorithms, relied upon by TLS. We port the mbedTLS library to OpenWSN, a 6TiSCH reference implementation, and deploy the code on the state-of-the-art OpenMote platform. We show that, when the peers use an ideal channel, the DTLS handshake uses up to 800 less and completes 0.6 s faster. Nonetheless, using an unreliable communication link, the DTLS handshake duration suffers a performance penalty of roughly 45%, while TLS’ handshake duration degrades by merely 15%. Similarly, the number of exchanged bytes doubles for DTLS while for TLS the increase is limited to 15%. The results indicate that IoT product developers should account for network characteristics when selecting a security protocol. Neglecting to do so can negatively impact the battery lifetime of the entire constrained network.

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    Sensors
    Article . 2021
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    Article . 2021
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