University of Luxembourg

About: University of Luxembourg is a education organization based out in Luxembourg, Luxembourg. It is known for research contribution in the topics: Context (language use) & Computer science. The organization has 4744 authors who have published 22175 publications receiving 381824 citations.

Automatically securing permission-based software by reducing the attack surface: an application to Android

Abstract: In the permission-based security model (used e.g. in Android and Blackberry), applications can be granted more permissions than they actually need, what we call a “permission gap”. Malware can leverage the unused permissions for achieving their malicious goals, for instance using code injection. In this paper, we present an approach to detecting permission gaps using static analysis. Using our tool on a dataset of Android applications, we found out that a non negligible part of applications suffers from permission gaps, i.e. does not use all the permissions they declare.

Cu(InGa)Se2 Solar Cells

Abstract: Cu(In,Ga)Se2 (CIGS) solar cells were characterized in cross section using electron beam induced current (EBIC) and synchrotron based x-ray fluorescence (XRF) measurements. Samples with varying gallium ratios and growth methods were compared. A correlation was observed between the compositional gallium grading profile from XRF and carrier activity seen in EBIC through the thickness of the CIGS layer. Samples with steep back grading showed carrier activity isolated near the CIGS/CdS interface, whereas a more uniform grading resulted in carrier activity seen throughout the absorber layer. 'Notch' grading showed only slight variation in EBIC profile compared to a back graded sample with similar gallium ratios.

Deep neural network with high-order neuron for the prediction of foamed concrete strength

Abstract: The article presents a deep neural network model for the prediction of the compressive strength of foamed concrete. A new, high-order neuron was developed for the deep neural network model to improve the performance of the model. Moreover, the cross-entropy cost function and rectified linear unit activation function were employed to enhance the performance of the model. The present model was then applied to predict the compressive strength of foamed concrete through a given data set, and the obtained results were compared with other machine learning methods including conventional artificial neural network (C-ANN) and second-order artificial neural network (SO-ANN). To further validate the proposed model, a new data set from the laboratory and a given data set of high-performance concrete were used to obtain a higher degree of confidence in the prediction. It is shown that the proposed model obtained a better prediction, compared to other methods. In contrast to C-ANN and SO-ANN, the proposed model can genuinely improve its performance when training a deep neural network model with multiple hidden layers. A sensitivity analysis was conducted to investigate the effects of the input variables on the compressive strength. The results indicated that the compressive strength of foamed concrete is greatly affected by density, followed by the water-to-cement and sand-to-cement ratios. By providing a reliable prediction tool, the proposed model can aid researchers and engineers in mixture design optimization of foamed concrete.

The Random Oracle Model and the Ideal Cipher Model Are Equivalent

Abstract: The Random Oracle Model and the Ideal Cipher Model are two well known idealised models of computation for proving the security of cryptosystems. At Crypto 2005, Coron et al.showed that security in the random oracle model implies security in the ideal cipher model; namely they showed that a random oracle can be replaced by a block cipher-based construction, and the resulting scheme remains secure in the ideal cipher model. The other direction was left as an open problem, i.e.constructing an ideal cipher from a random oracle. In this paper we solve this open problem and show that the Feistel construction with 6 rounds is enough to obtain an ideal cipher; we also show that 5 rounds are insufficient by providing a simple attack. This contrasts with the classical Luby-Rackoff result that 4 rounds are necessary and sufficient to obtain a (strong) pseudo-random permutation from a pseudo-random function.

Aspect-oriented multi-view modeling

Abstract: Multi-view modeling allows a developer to describe a software system from multiple points of view, e.g. structural and behavioral, using different modeling notations. Aspect-oriented modeling techniques have been proposed to address the scalability problem within individual modeling notations. This paper presents RAM, an aspect-oriented modeling approach that provides scalable multi-view modeling. RAM allows the modeler to define stand-alone reusable aspect models using 3 modeling notations. The aspect models support the modeling of structure (using UML class diagrams) and behavior (using UML state and sequence diagrams). RAM supports aspect dependency chains, which allows an aspect providing complex functionality to reuse the functionality provided by other aspects. The RAM weaver can create woven views of the composed model for debugging, simulation or code generation purpose, as well as perform consistency checks during the weaving and on the woven model to detect inconsistencies of the composition.