Improving virus protection with an efficient secure architecture with memory encryption, integrity and information leakage protection

Malicious software and other attacks are a major concern in the computing ecosystem and there is a need to go beyond the answers based on untrusted software. Trusted and secure computing can add a new hardware dimension to software protection. Several secure computing hardware architectures using memory encryption and memory integrity checkers have been proposed during the past few years to provide applications with a tamper resistant environment. Some solutions, such as HIDE, have also been proposed to solve the problem of information leakage on the address bus. We propose the CRYPTOPAGE architecture which implements memory encryption, memory integrity protection checking and information leakage protection together with a low performance penalty (3% slowdown on average) by combining the Counter Mode of operation, local authentication values and MERKLE trees. It has also several other security features such as attestation, secure storage for applications and program identification. We present some applications of the CRYPTOPAGE architecture in the computer virology field as a proof of concept of improving security in presence of viruses compared to software only solutions.     

Distinctive arsenic(V) trapping modes by magnetite nanoparticles induced by different sorption processes

Arsenic sorption onto iron oxide spinels such as magnetite may contribute to arsenic immobilization at redox fronts in soils, sediments, and aquifers, as well as in putative remediation and water treatment technologies. We have investigated As(V) speciation resulting from different sorption processes on magnetite nanoparticles, including both adsorption and precipitation, using X-ray absorption fine structure (XAFS) spectroscopy and transmission electron microscopy (TEM). XAFS results suggest that AsO(4) tetrahedra form predominantly inner-sphere bidentate corner-sharing ((2)C) complexes and outer-sphere complexes on magnetite in the adsorption experiments. In the precipitation experiments, an increasing fraction of AsO(4) tetrahedra appears to be incorporated in clusters having a magnetite-like local structure with increasing As loading, the remaining fraction of As being adsorbed at the surface of magnetite particles. In the sample with the highest As loading (15.7 μmol/m(2)) XAFS data indicate that As(V) is fully incorporated in such clusters. Such processes help to explain the significantly higher arsenic uptake in precipitation samples compared to those generated in adsorption experiments. In addition, for the precipitation samples, TEM observations indicate the formation of amorphous coatings and small (~3 nm) nanoparticles associated with larger (~20-40 nm) magnetite nanoparticles, which are absent in the adsorption samples. These results suggest that As(V) could form complexes at the surfaces of the small nanoparticles and could be progressively incorporated in their structure with increasing As loading. These results provide some of the fundamental knowledge about As(V)-magnetite interactions that is essential for developing effective water treatment technologies for arsenic.     

Fuzzy inference systems: An integrated modeling environment for collaboration between expert knowledge and data using FisPro

The present paper aims to demonstrate the interest of fuzzy inference systems in system modeling when human interaction is important. It discusses the originality of FIS and their capability to integrate expertise and rule learning from data into a single framework, analyzing their place relatively to concurrent approaches. An open source software implementation is presented, with a focus on the useful features for modeling. Two real world case studies are presented to illustrate the approach and the software utility.     

Transparent polycrystalline alumina obtained by SPS: Green bodies processing effect

Starting from a commercial slurry of high purity α-Al₂O₃, freeze-dried powders, cast, filter-pressed or cold isostatically pressed samples were produced. Resulting powders or green bodies showing different particles packing were densified by spark plasma sintering (SPS) to obtain transparent polycrystalline α-Al₂O₃. Microstructure and real in-line transmittance (RIT) after SPS were dependent on the particles packing quality. Avoiding large agglomerates, narrowing the pore size distribution, reducing the most-frequent pore size (D_mode) and avoiding macroscopic heterogeneities within the green bodies enabled high RIT values to be achieved in the visible and near-infrared spectrum. However, a limit was achieved in the preparation of green bodies for which reducing the D_mode had no more influence on the optical behaviour of samples sintered by SPS. Finally, pure α-Al₂O₃ samples presenting a high RIT_640 nm value of 53% were produced from all the green bodies obtained by the following techniques: filter-pressing, slip casting and cold isostatic pressing.     

A novel statistical generative model dedicated to face recognition

In this paper, a novel statistical generative model to describe a face is presented, and is applied to the face authentication task. Classical generative models used so far in face recognition, such as Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs) for instance, are making strong assumptions on the observations derived from a face image. Indeed, such models usually assume that local observations are independent, which is obviously not the case in a face. The presented model hence proposes to encode relationships between salient facial features by using a static Bayesian Network. Since robustness against imprecisely located faces is of great concern in a real-world scenario, authentication results are presented using automatically localised faces. Experiments conducted on the XM2VTS and the BANCA databases showed that the proposed approach is suitable for this task, since it reaches state-of-the-art results. We compare our model to baseline appearance-based systems (Eigenfaces and Fisherfaces) but also to classical generative models, namely GMM, HMM and pseudo-2DHMM.     

SYBR®Green qPCR screening methods for the presence of “35S promoter” and “NOS terminator” elements in food and feed products

The Cauliflower Mosaic Virus “35S promotor” (p35S) and the Agrobacterium “Nopaline Synthase” terminator (tNOS) are the most represented generic recombinant elements in commercial genetically modified crops to date. A set of four new SYBR^®Green qPCR methods targeting the “p35S” and “tNOS” core elements have been developed. These qPCR methods generate short amplicons of 147 and 75 bp for the “p35S” element and 172 and 69 bp for the “tNOS” element. Single target plasmids containing these amplicons were constructed and allow determining the nominal melting temperature (T _m value) of each amplicon. The four methods are specific for their respective targets, and moreover, three of them are highly sensitive (up to 1–2 copies detectable) at a PCR efficiency ranging between 95 and 100%. The latter methods can detect their respective targets at 0.1% (w/w) gDNA levels and are suitable for detecting low levels of genetically modified materials containing the “p35S” and/or “tNOS” elements.     

An integrated on chip organic optical source based on electrochemiluminescence

We report here on an original “glass-PDMS-glass” electrochemiluminescence optofluidic device that integrates 32 electrodes on a large surface. This organic electrically powered light source has been integrated on chip using an alternative process based on a photopatternable silicone resist. Under a continuous flow rate, this device allows electrogenerated chemiluminescence for several minutes which would be very useful for future μTAS systems.