Mean field analysis for Continuous Time Bayesian Networks

In this paper we investigate the use of the mean field technique to analyze Continuous Time Bayesian Networks (CTBN). They model continuous time evolving variables with exponentially distributed transition rates depending on the parent variables in the graph. CTBN inference consists of computing the probability distribution of a subset of variables, conditioned by the observation of other variables' values (evidence). The computation of exact results is often unfeasible due to the complexity of the model. For such reason, the possibility to perform the CTBN inference through the equivalent Generalized Stochastic Petri Net (GSPN) was investigated in the past. In this paper instead, we explore the use of mean field approximation and apply it to a well-known epidemic case study. The CTBN model is converted in both a GSPN and in a mean field based model. The example is then analyzed with both solutions, in order to evaluate the accuracy of the mean field approximation for the computation of the posterior probability of the CTBN given an evidence. A summary of the lessons learned during this preliminary attempt concludes the paper

The GreatSPN tool: recent enhancements

GreatSPN is a tool that supports the design and the qualitative and quantitative analysis of Generalized Stochastic Petri Nets (GSPN) and of Stochastic Well-Formed Nets (SWN). The very first version of GreatSPN saw the light in the late eighties of last century: since then two main releases where developed and widely distributed to the research community: GreatSPN1.7 [13], and GreatSPN2.0 [8]. This paper reviews the main functionalities of GreatSPN2.0 and presents some recently added features that significantly enhance the efficacy of the tool

A Bayesian Network Approach for the Interpretation of Cyber Attacks to Power Systems

The focus of this paper is on the analysis of the cyber security resilience of digital infrastructures deployed by power grids, internationally recognized as a priority since several recent cyber attacks targeted energy systems and in particular the power service. In response to the regulatory framework, this paper presents an analysis approach based on the Bayesian Networks formalism and on real world threat scenarios. Our approach enables analyses oriented to planning of security measures and monitoring, and to forecasting of adversarial behaviours

Multi-class queuing networks models for energy optimization

The increase of energy consumption and the related costs in large data centers has stimulated new researches on techniques to optimize the power consumption of the servers. In this paper we focus on systems that should process a peak workload consisting of different classes of applications. The objective is to implement a policy of load control which allows an efficient use of the power deployed to the resources. The proposed strategy controls the workload mix in order to achieve the maximum utilization of all the resources allocated. As a consequence, the power provision will be fully utilized and the throughput maximized. Thus, the costs to execute a given workload will be minimized, together with its energy consumption, since the required processing time is decreased

Automatic Moment-Closure Approximation of Spatially Distributed Collective Adaptive Systems

Spatially distributed collective adaptive systems are an important class of systems that pose significant challenges to modeling due to the size and complexity of their state spaces. This problem is acute when the dynamic behavior of the system must be captured, such as to predict system performance. In this article, we present an abstraction technique that automatically derives a moment-closure approximation of the dynamic behavior of a spatially distributed collective adaptive system from a discrete representation of the entities involved. The moment-closure technique is demonstrated to give accurate estimates of dynamic behavior, although the number of ordinary differential equations generated for the second-order joint moments can grow large in some cases. For these cases, we propose a rigorous model reduction technique and demonstrate its use to substantially reduce the computational effort with only limited impact on the accuracy if the reduction threshold is set appropriately. All techniques reported in this article are implemented in a tool that is freely available for download

Analysis of new control applications

This document reports the results of the activities performed during the first year of the CRUTIAL project, within the Work Package 1 "Identification and description of Control System Scenarios". It represents the outcome of the analysis of new control applications in the Power System and the identification of critical control system scenarios to be explored by the CRUTIAL project

Analisi e rilevamento intelligente di processi di attacco alle Smart-Grid

Proponiamo una metodologia basata sulle Reti Bayesiane come strumento di supporto all’analisi della sicurezza di Smart Grid, ed in particolare per la previsione di intrusioni e attività ostili