Exploiting Partial Symmetries for Markov Chain Aggregation

International audience; The technique presented in this paper allows the automatic construction of a lumped Markov chain for almost symmetrical Stochastic Well-formed Net (SWN) models. The starting point is the Extended Symbolic Reachability Graph (ESRG), which is a reduced representation of a SWN model reachability graph (RG), based on the aggregation of states into classes. These classes may be used as aggregates for lumping the Continuous Time Markov Chain (CTMC) isomorphic to the model RG: however it is not always true that the lumpability condition is verified by this partition of states. In the paper we propose an algorithm that progressively refines the ESRG classes until a lumped Markov chain is obtained

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

Bounds Computation for Symmetric Nets

Monotonicity in Markov chains is the starting point for quantitative abstraction of complex probabilistic systems leading to (upper or lower) bounds for probabilities and mean values relevant to their analysis. While numerous case studies exist in the literature, there is no generic model for which monotonicity is directly derived from its structure. Here we propose such a model and formalize it as a subclass of Stochastic Symmetric (Petri) Nets (SSNs) called Stochastic Monotonic SNs (SMSNs). On this subclass the monotonicity is proven by coupling arguments that can be applied on an abstract description of the state (symbolic marking). Our class includes both process synchronizations and resource sharings and can be extended to model open or cyclic closed systems. Automatic methods for transforming a non monotonic system into a monotonic one matching the MSN pattern, or for transforming a monotonic system with large state space into one with reduced state space are presented. We illustrate the interest of the proposed method by expressing standard monotonic models and modelling a flexible manufacturing system case study

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

Multiple Sclerosis disease: a computational approach for investigating its drug interactions

Multiple Sclerosis (MS) is a chronic and potentially highly disabling disease that can cause permanent damage and deterioration of the central nervous system. In Europe it is the leading cause of non-traumatic disabilities in young adults, since more than 700,000 EU people suffer from MS. Although recent studies on MS pathophysiology have been provided, MS remains a challenging disease. In this context, thanks to recent advances in software and hardware technologies, computational models and computer simulations are becoming appealing research tools to support scientists in the study of such disease. Thus, motivated by this consideration we propose in this paper a new model to study the evolution of MS in silico, and the effects of the administration of Daclizumab drug, taking into account also spatiality and temporality of the involved phenomena. Moreover, we show how the intrinsic symmetries of the system can be exploited to drastically reduce the complexity of its analysis.Comment: Submitted to CIBB 2019 post proceedings - LNC

Experimental Validation of Architectural Solutions

In this deliverable the experimental results carried out in four different contexts are reported. The first contribution concerns an experimental campaign performed using the AJECT (Attack inJECTion) tool able to emulate different types of attackers behaviour and to collect information on the effect of such attacks on the target system performance. This tool is also used to perform some of the experiments described in the fourth part of the deliverable. The second contribution concerns a complementary approach using honeypots to cap- ture traces of attacker behaviours, to then study and characterize them. Different kinds of honeypots were deployed in the described experiments: low-interaction and high-interaction ones, exposing different kinds of services and protocols (general purpose network services as well as SCADA specific ones). The third and fourth contribution refer to experiments conducted on some com- ponents of the CRUTIAL architecture, namely FOSEL (Filtering with the help of Overlay Security Layer), the CIS-CS (Communication Service) and the CIS-PS (Protection Service). The experiments have been performed with the aim of evaluating the effectiveness of the proposed components from the point of view of the dependability improvement they bring, as well as the performance overhead introduced by their implementation.Project co-funded by the European Commission within the Sixth Framework Programme (2002-2006