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.

Side Channel Cryptanalysis of a Higher Order Masking Scheme

Abstract: In the recent years, DPA attacks have been widely investigated. In particular, 2-nd order DPA have been improved and successfully applied to break many masked implementations. In this context a higher order masking scheme has been proposed by Schramm and Paar at CT-RSA 2006. The authors claimed that the scheme is resistant against d-th order DPA for any arbitrary chosen order d. In this paper, we prove that this assertion is false and we exhibit several 3-rd order DPA attacks that can defeat Schramm and Paar's countermeasure for any value of d.

Exploring Chemical Compound Space with Quantum-Based Machine Learning

Abstract: Rational design of compounds with specific properties requires conceptual understanding and fast evaluation of molecular properties throughout chemical compound space (CCS) -- the huge set of all potentially stable molecules. Recent advances in combining quantum mechanical (QM) calculations with machine learning (ML) provide powerful tools for exploring wide swaths of CCS. We present our perspective on this exciting and quickly developing field by discussing key advances in the development and applications of QM-based ML methods to diverse compounds and properties and outlining the challenges ahead. We argue that significant progress in the exploration and understanding of CCS can be made through a systematic combination of rigorous physical theories, comprehensive synthetic datasets of microscopic and macroscopic properties, and modern ML methods that account for physical and chemical knowledge.

Empirical assessment of machine learning-based malware detectors for Android

Abstract: To address the issue of malware detection through large sets of applications, researchers have recently started to investigate the capabilities of machine-learning techniques for proposing effective approaches. So far, several promising results were recorded in the literature, many approaches being assessed with what we call in the lab validation scenarios. This paper revisits the purpose of malware detection to discuss whether such in the lab validation scenarios provide reliable indications on the performance of malware detectors in real-world settings, aka in the wild. To this end, we have devised several Machine Learning classifiers that rely on a set of features built from applications' CFGs. We use a sizeable dataset of over 50 000 Android applications collected from sources where state-of-the art approaches have selected their data. We show that, in the lab, our approach outperforms existing machine learning-based approaches. However, this high performance does not translate in high performance in the wild. The performance gap we observed--F-measures dropping from over 0.9 in the lab to below 0.1 in the wild--raises one important question: How do state-of-the-art approaches perform in the wild?

The Role of Perceived Stress and Self-Efficacy in Young People’s Life Satisfaction: A Longitudinal Study

Abstract: Life satisfaction is an important indicator of successful development. However, adolescents’ life satisfaction tends to be relatively unsteady, and environmental influences play a critical role in shaping life satisfaction among adolescents in the transition to young adulthood. Given the paramount importance that education plays in adolescents’ lives, adolescents’ life satisfaction may vary as a function of school-related stress experience. At the same time, coping resources may help reduce adverse effects of stress on life satisfaction. With this in mind, we examined whether, and to what extent, perceived stress in education and general self-efficacy (a resource that facilitates coping) affect the life satisfaction of adolescents in transition to young adulthood. We distinguished between baseline levels of stress and self-efficacy and within-person change in stress and self-efficacy to determine whether life satisfaction is sensitive to fluctuations in stress and self-efficacy when person-specific levels of stress and self-efficacy are taken into account. Estimating growth curve models on data from a panel study on the life trajectories of compulsory-school leavers (n = 5126, 55.3 % female), we found that baseline levels of stress and self-efficacy, as well as within-person change in stress and self-efficacy, affected adolescents’ life satisfaction. Moreover, our results showed that baseline self-efficacy mitigated the negative effect of baseline stress on life satisfaction. These findings improve our understanding of two major psychological determinants of adolescents’ life satisfaction and extend our knowledge of life satisfaction trajectories during the transition to young adulthood.

An Efficient Method for Random Delay Generation in Embedded Software

Abstract: Random delays are a countermeasure against a range of side channel and fault attacks that is often implemented in embedded software. We propose a new method for generation of random delays and a criterion for measuring the efficiency of a random delay countermeasure. We implement this new method along with the existing ones on an 8-bit platform and mount practical side-channel attacks against the implementations. We show that the new method is significantly more secure in practice than the previously published solutions and also more lightweight.