Advanced persistent threats (APTs) have rocketed over the last years. Unfortunately, their technical characterization is incomplete--it is still unclear if they are advanced usages of regular malware or a different form of malware. This is key to develop an effective cyberdefense. To address this issue, in this paper we analyze the techniques and tactics at stake for both regular and APT-linked malware. To enable reproducibility, our approach leverages only publicly available datasets and analysis tools. Our study involves 11,651 regular malware and 4686 APT-linked ones.

This manuscript relates to the exploiting of the abstract calculus pattern (ACP) for the (numerical) solution of ordinary differential equation (ODEs) systems, which are ubiquitous mathematical formulations of many physical (dynamical) phenomena. We present FOODIE, a software suite aimed to numerically solve ODE problems by means of a clear, concise, and efficient abstract interface.

Background: The oral food challenge test (OFC) represents the gold standard for the diagnosis of food allergy (FA). It is also necessary for the safe reintroduction of the allergen into the diet of the patient. However, it is burdened with serious side effects.

Graphical models are well-known mathematical objects for describing conditional dependency relationships between random variables of a complex system. Gaussian graphical models refer to the case of multivariate Gaussian variable for which the graphical model is encoded through the support of corresponding inverse covariance (precision) matrix. We consider a problem of estimating multiple Gaussian graphical models from high- dimensional data sets under the assumption that they share the same conditional independence structure. However, the individual correlation matrices can differ.

This paper considers a Multiple Objective variant of the Critical Disruption Path problem to extend its suitability in a range of security operations relying on path-based network interdiction, including flight pattern optimisation for surveillance.

Nowadays, Predictive Maintenance is a mandatory tool to reduce the cost of production in the semiconductor industry. This paper considers as a case study a critical part of the electrochemical deposition system, namely, the four Pins that hold a wafer inside a chamber. The aim of the study is to replace the schedule of replacement of Pins presently based on fixed timing (Preventive Maintenance) with a Hardware/Software system that monitors the conditions of the Pins and signals possible conditions of failure (Predictive Maintenance).

Exploiting the Infrared Atmospheric Sounder Interferometer (IASI) profiling capability for surface parameters, atmospheric temperature, and water vapour we have designed a new Water Deficit Index (wdi) to monitor drought and heatwaves. Because of climate change at a global level, drought is becoming a strong emergency also in countries which never experienced it, such as the Mediterranean mid-latitude area and, in particular, Italy. The last two years strongly affected the northern part of Italy, i.e. the Po Valley, causing high vegetation and soil water stress.

In this work, we propose and explore a novel network-constraint survival methodology considering the Weibull accelerated failure time (AFT) model combined with a penalized likelihood approach for variable selection and estimation [2]. Our estimator explicitly incorporates the correlation patterns among predictors using a double penalty that promotes both sparsity and the grouping effect. In or- der to solve the structured sparse regression problems we present an efficient iterative computational algorithm based on proximal gradient descent method [1].

Surface and vegetation monitoring is a key activity in analyzing and understanding how climate change is impacting natural resources. Moreover, identifying vegetation stress using remote-sensed data has proven to be essential in assessing said understanding, as well as in the effort to prevent or act upon extreme phenomena, such as premature land and forest dryness due to summer heatwaves in the Mediterranean area.

This paper concerns the study of a non-smooth logistic regression function. The focus is on a high-dimensional binary response case by penalizing the decomposition of the unknown logit regression function on a wavelet basis of functions evaluated on the sampling design. Sample sizes are arbitrary (not necessarily dyadic) and we consider general designs. We study separable wavelet estimators, exploiting sparsity of wavelet decompositions for signals belonging to homogeneous Besov spaces, and using efficient iterative proximal gradient descent algorithms.