Machine-assisted Cyber Threat Analysis using Conceptual Knowledge Discovery

Over the last years, computer networks have evolved into highly dynamic and interconnected environments, involving multiple heterogeneous devices and providing a myriad of services on top of them. This complex landscape has made it extremely difficult for security administrators to keep accurate and be effective in protecting their systems against cyber threats. In this paper, we describe our vision and scientific posture on how artificial intelligence techniques and a smart use of security knowledge may assist system administrators in better defending their networks. To that end, we put forward a research roadmap involving three complimentary axes, namely, (I) the use of FCA-based mechanisms for managing configuration vulnerabilities, (II) the exploitation of knowledge representation techniques for automated security reasoning, and (III) the design of a cyber threat intelligence mechanism as a CKDD process. Then, we describe a machine-assisted process for cyber threat analysis which provides a holistic perspective of how these three research axes are integrated together

Nanocomposite MFI-alumina and FAU-alumina Membranes: Synthesis, Characterization and Application to Paraffin Separation and CO2 Capture

Rouleau, L. Pirngruber, G. Guillou, F. Barrere-Tricca, C. Omegna, A. Valtchev, V. Pera-Titus, M. Miachon, S. Dalmon, J. A.International audienceIn this work, we report the preparation of thermally and mechanically resistant high-surface (24-cm2) nanocomposite MFI-alumina and FAUalumina membranes by pore-plugging synthesis inside the macropores of α-alumina multilayered tubular supports. The MFI membranes were prepared from a clear solution precursor mixture being able to easily penetrate into the pores of the support. The MFI membranes were evaluated in the separation of n-/i-butane mixtures. The synthesis reliability was improved by mild stirring. The most selective MFI membranes were obtained for supports with mean pore sizes of 0.2 and 0.8 μm. The MFI effective thickness could be reduced to less than 10 μm by impregnating the support with water prior to synthesis and by diluting the synthesis mixture. The best MFI membrane offered an excellent tradeoff between selectivity and permeance at 448 K, with separation factors for equimolar n-butane/i-butane mixtures up to 18 and n-butane mixture permeances as high as 0.7 μmol$\cdot$s-1$\cdot$m-2$\cdot$Pa-1.Furthermore, a novel nanocomposite FAU membrane architecture has been obtained by an original synthesis route including in situ seeding using a cold gel-like precursor mixture, followed by growth of the FAU material by hydrothermal synthesis in two steps using a clear solution of low viscosity. This new membrane showed interesting performance in the separation of an equimolar CO2/N2 mixture at 323 K, with CO2/N2 separation factors and mixture CO2 permeances up to 12 and 0.4 μmol$\cdot$s-1$\cdot$m-2$\cdot$Pa-1,respectively

Assessment of bone ingrowth potential of biomimetic hydroxyapatite and brushite coated porous E-beam structures

The bone ingrowth potential of biomimetic hydroxyapatite and brushite coatings applied on porous E-beam structure was examined in goats and compared to a similar uncoated porous structure and a conventional titanium plasma spray coating. Specimens were implanted in the iliac crest of goats for a period of 3 (4 goats) or 15 weeks (8 goats). Mechanical implant fixation generated by bone ingrowth was analyzed by a push out test. Histomorphometry was performed to assess the bone ingrowth depth and bone implant contact. The uncoated and hydroxyapatite-coated cubic structure had significantly higher mechanical strength at the interface compared to the Ti plasma spray coating at 15 weeks of implantation. Bone ingrowth depth was significantly larger for the hydroxyapatite- and brushite-coated structures compared to the uncoated structure. In conclusion, the porous E-beam surface structure showed higher bone ingrowth potential compared to a conventional implant surface after 15 weeks of implantation. Addition of a calcium phosphate coating to the E-beam structure enhanced bone ingrowth significantly. Furthermore, the calcium phosphate coating appears to work as an accelerator for bone ingrowth

Toward Smart Implant Synthesis: Bonding Bioceramics of Different Resorbability to Match Bone Growth Rates

This work was partially funded by the European Union (Project MARMED - 2011-1/164), the Spanish Government and FEDER (CICYT MAT2006-10481) by Xunta de Galicia (CN2012/292)

Evaluating the performance of coupled snow–soil models in SURFEXv8 to simulate the permafrost thermal regime at a high Arctic site

Climate change projections still suffer from a limited representation of the permafrost–carbon feedback. Predicting the response of permafrost temperature to climate change requires accurate simulations of Arctic snow and soil properties. This study assesses the capacity of the coupled land surface and snow models ISBA-Crocus and ISBA-ES to simulate snow and soil properties at Bylot Island, a high Arctic site. Field measurements complemented with ERA-Interim reanalyses were used to drive the models and to evaluate simulation outputs. Snow height, density, temperature, thermal conductivity and thermal insulance are examined to determine the critical variables involved in the soil and snow thermal regime. Simulated soil properties are compared to measurements of thermal conductivity, temperature and water content. The simulated snow density profiles are unrealistic, which is most likely caused by the lack of representation in snow models of the upward water vapor fluxes generated by the strong temperature gradients within the snowpack. The resulting vertical profiles of thermal conductivity are inverted compared to observations, with high simulated values at the bottom of the snowpack. Still, ISBA-Crocus manages to successfully simulate the soil temperature in winter. Results are satisfactory in summer, but the temperature of the top soil could be better reproduced by adequately representing surface organic layers, i.e., mosses and litter, and in particular their water retention capacity. Transition periods (soil freezing and thawing) are the least well reproduced because the high basal snow thermal conductivity induces an excessively rapid heat transfer between the soil and the snow in simulations. Hence, global climate models should carefully consider Arctic snow thermal properties, and especially the thermal conductivity of the basal snow layer, to perform accurate predictions of the permafrost evolution under climate change