UML class diagrams supporting formalism definition in the Draw-Net Modeling System

The Draw-Net Modeling System (DMS) is a customizable framework supporting the design and the solution of models expressed in any graph-based formalism, thanks to an open architecture. During the years, many formalisms (Petri Nets, Bayesian Networks, Fault Trees, etc.) have been included in DMS. A formalism defines all the primitives that can be used in a model (nodes, arcs, properties, etc.) and is stored into XML files. The paper describes a new way to manage formalisms: the user can create a new formalism by drawing a UML Class Diagrams (CD); then the corresponding XML files are automatically generated. If instead the user intends to edit an existing formalism, a "reverse engineering" function generates the CD from the XML files. The CD can be handled inside DMS, and acts an intuitive and graphical "meta-model" to represent the formalism. An application example is presented

The Conversion of Dynamic Fault Trees to Stochastic Petri Nets, as a case of Graph Transformation

AbstractA model-to-model transformation from Dynamic Fault Trees to Stochastic Petri Nets, by means of graph transformation rules, is presented in this paper. Dynamic Fault Trees (DFT) are used for the reliability analysis of complex and large systems and represent by means of gates, how combinations or sequences of component failure events, lead to the failure of the system. DFTs need the state space solution which can be obtained by converting a DFT to a Stochastic Petri Net: this task is expressed by means of graph transformation rules, and is applied to a case of system

Extended Fault Trees Analysis supported by Stochastic Petri Nets

This work presents several extensions to the Fault Tree [90] formalism used to build models oriented to the Dependability [103] analysis of systems. In this way, we increment the modelling capacity of Fault Trees which turn from simple combinatorial models to an high level language to represent more complicated aspects of the behaviour and of the failure mode of systems. Together with the extensions to the Fault Tree formalism, this work proposes solution methods for extended Fault Trees in order to cope with the new modelling facilities. These methods are mainly based on the use of Stochastic Petri Nets. Some of the formalisms described in this work are already present in the literature; for them we propose alternative solution methods with respect to the existing ones. Other formalisms are instead part of the original contribution of this work

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

Representing domains and scenarios by means of model replication and composition

We consider a domain as a particular system or a portion of the system, while a scenario is a sequence of effects on the domain, originated by a particular event or condition. We show how it is possible to build first the model of the domain by replication and composition of atomic models, each representing a particular aspect of the domain. Then, the models of the scenarios are obtained from the domain\u2019s model, by composing further atomic models representing the events originating the scenarios. In particular, we take into account the domain consisting of one control centre and a set of substations inside an electrical distribution grid, communicating by means of a network. We consider scenarios originated by threats such as the denial of service attack to the communication network, and the temporary unavailability of substations due to the failure and the repair of the internal components. Stochastic Activity Networks (SAN) are the modelling formalism. The simulation of the models representing the scenarios, estimates the impact of the threats on the communication reliability

Simulating the communication of commands and signals in a distribution grid

The report presents the simulation of communication scenarios involving one area control centre and a set of substations inside a distribution grid of the Electrical Power System. In such scenarios, the communication is affected by threats different from those under exam in [1, 2]; in particular, here, we consider the denial of service attack to the communication network, and the temporary internal failure of a subset of substations. The scenarios have been modeled and simulated in form of Stochastic Activity Networks (SAN); the goal is the evaluation of the impact of the threats, on the communication reliability

DBNet, a tool to convert Dynamic Fault Trees into Dynamic Bayesian Networks

The unreliability evaluation of a system including dependencies involving the state of components or the failure events, can be performed by modelling the system as a Dynamic Fault Tree (DFT). The combinatorial technique used to solve standard Fault Trees is not suitable for the analysis of a DFT. The conversion into a Dynamic Bayesian Network (DBN) is a way to analyze a DFT. This paper presents a software tool allowing the automatic analysis of a DFTexploiting its conversion to a DBN. First, the architecture of the tool is described, together with the rules implemented in the tool, to convert dynamic gates in DBNs. Then, the tool is tested on a case of system: its DFT model and the corresponding DBN are provided and analyzed by means of the tool. The obtained unreliability results are compared with those returned by other tools, in order to verify their correctness. Moreover, the use of DBNs allows to compute further results on the model, such as diagnostic and sensitivity indices

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

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