Rubin-Euclid Derived Data Products:Initial Recommendations

This report is the result of a joint discussion between the Rubin and Euclid scientific communities. The work presented in this report was focused on designing and recommending an initial set of Derived Data products (DDPs) that could realize the science goals enabled by joint processing. All interested Rubin and Euclid data rights holders were invited to contribute via an online discussion forum and a series of virtual meetings. Strong interest in enhancing science with joint DDPs emerged from across a wide range of astrophysical domains: Solar System, the Galaxy, the Local Volume, from the nearby to the primaeval Universe, and cosmology

Authenticating IDS Autoencoders Using Multipath Neural Networks

International audienceAn Intrusion Detection System (IDS) is a core element for securing critical systems. An IDS can use signatures of known attacks, or an anomaly detection model for detecting unknown attacks. Attacking an IDS is often the entry point of an attack against a critical system. Consequently, the security of IDSs themselves is imperative. To secure model-based IDSs, we propose a method to authenticate the anomaly detection model. The anomaly detection model is an autoencoder for which we only have access to input-output pairs. Inputs consist of time windows of values from sensors and actuators of an Industrial Control System. Our method is based on a multipath Neural Network (NN) classifier, a newly proposed deep learning technique. The idea is to characterize errors of an IDS's autoencoder by using a multipath NN's confidence measure c. We use the Wilcoxon-Mann-Whitney (WMW) test to detect a change in the distribution of the summary variable c, indicating that the autoencoder is not working properly. We compare our method to two baselines. They consist in using other summary variables for the WMW test. We assess the performance of these three methods using simulated data. Among others, our analysis shows that: 1) both baselines are oblivious to some autoencoder spoofing attacks while 2) the WMW test on a multipath NN's confidence measure enables detecting eventually any autoencoder spoofing attack

Active Learning of Markov Decision Processes using Baum-Welch algorithm

Cyber-physical systems (CPSs) are naturally modelled as reactive systems with nondeterministic and probabilistic dynamics. Model-based verification techniques have proved effective in the deployment of safety-critical CPSs. Central for a successful application of such techniques is the construction of an accurate formal model for the system. Manual construction can be a resource-demanding and error-prone process, thus motivating the design of automata learning algorithms to synthesise a system model from observed system behaviours. This paper revisits and adapts the classic Baum-Welch algorithm for learning Markov decision processes and Markov chains. For the case of MDPs, which typically demand more observations, we present a model-based active learning sampling strategy that choses examples which are most informative w.r.t. the current model hypothesis. We empirically compare our approach with state-of-the-art tools and demonstrate that the proposed active learning procedure can significantly reduce the number of observations required to obtain accurate models.</p

An MM Algorithm to Estimate Parameters in Continuous-Time Markov Chains

Prism and Storm are popular model checking tools that provide a number of powerful analysis techniques for Continuous-time Markov chains (CTMCs). The outcome of the analysis is strongly dependent on the parameter values used in the model which govern the timing and probability of events of the resulting CTMC. However, for some applications, parameter values have to be empirically estimated from partially-observable executions. In this work, we address the problem of estimating parameter values of CTMCs expressed as Prism models from a number of partially-observable executions which might possibly miss some dwell time measurements. The semantics of the model is expressed as a parametric CTMC (pCTMC), i.e., CTMC where transition rates are polynomial functions over a set of parameters. Then, building on a theory of algorithms known by the initials MM, for minorization–maximization, we present an iterative maximum likelihood estimation algorithm for pCTMCs. We present an experimental evaluation of the proposed technique on a number of CTMCs from the quantitative verification benchmark set. We conclude by illustrating the use of our technique in a case study: the analysis of the spread of COVID-19 in presence of lockdown countermeasures.</p

Science hackathons for cyberphysical system security research: putting CPS tesdbed platforms to good use

International audienceA challenge is to develop cyber-physical system scenarios that reflect the diversity and complexity of real-life cyber-physical systems in the research questions that they address. Time-bounded collaborative events, such a hackathons, jams and sprints, are increasingly used as a means of bringing groups of individuals together , in order to explore challenges and develop solutions. This paper describes our experiences, usign a science hackathon to bring individual researchers together, in order to develop a common usecase implemented on a shared CPS testeb platform that embodies the diversity in their own security research questions. A qualitative study of the event was conducted, in order to evaluate the success of the process, with a view to improving future similar event

Hybrid molecules inhibiting myeloperoxidase activity and serotonin reuptake: a possible new approach of major depressive disorders with inflammatory syndrome.

Major depressive disorder (MDD) is accompanied with an imbalance in the immune system and cardiovascular impairments, such as atherosclerosis. Several mechanisms have been pointed out to underlie this rather unexpected association, and among them the activity of myeloperoxidase (MPO). The aim of our study was to find compounds that inhibit both MPO and serotonin transporter (SERT) for treating MDD associated with cardiovascular diseases.JOURNAL ARTICLESCOPUS: ar.jFLWINinfo:eu-repo/semantics/publishe