As power grids transition into decentralized Cyber-Physical Power Systems, the digitalization of legacy devices has dramatically multiplied the potential entry points for cyber adversaries. To accurately assess these threats, security models must capture both detailed attack steps and network timing effects. However, obtaining real-world cyber incident data to parameterize these models is rarely possible due to strict privacy and confidentiality constraints within utility companies. To overcome this limitation, we present a simulation-based framework that integrates Attack Graphs (AGs) with the OMNeT++ Discrete Event Simulation environment and Dynamic Bayesian Networks (DBNs). Attack step logic is modeled through Control Finite State Machines, allowing for dynamic interactions between attacker behavior and simulated network components. The simulation automatically generates time-series data used to train AG-derived DBNs used to answer probabilistic queries on attack progression. We present a case study on a Distributed Energy Resource cyberattack which demonstrates that the framework captures complex temporal dependencies and maintains high predictive accuracy supporting cybersecurity assessment for critical energy infrastructures. The code and models implemented in this work are available here: https://github.com/Dosclic98/dbn-sim-learning.git.

Agent-Based Modeling and Simulation (ABMS) has been increasingly applied in various research fields, thanks to the capability of these models to describe fine-grained realworld behavior and to the ease of interpretation by domain experts. However, such models lack a formal definition and well-defined semantics that are common to the different tools supporting ABMS. This may occasionally lead to greater complexity in interpreting the results with respect to other modeling approaches. To address this issue, an ABM semantics that adopts a continuous-time approach and a next-event time advance simulation algorithm has been formally defined and presented. Such an approach may lead to high computation times as it requires recalculations of activity rates for all the agents after each event. In this preliminary study, we exploit the FLAME GPU framework to evaluate the benefits that GPU computing may bring to the performance of our simulation algorithm.

The immune system (IS) represents a complex network of cells and molecules devoted to the protection of individuals from external pathogens, and in terms of complexity, it is only second to the central nervous system. As our knowledge of the IS mechanisms has become more exhaustive, interest has grown in applying modeling and simulation techniques in this context. In particular, among these techniques, the Agent Based Models (ABMs) have been increasingly applied for the IS simulation. One of the major drawbacks of ABMs is represented by the lack of well-defined semantics, which may lead to inconsistent results in comparison to other stochastic approaches. In this paper, we make use of the well-defined semantics and the simulation algorithm for ABMs that we proposed in [1] to implement a few models of the Cancer-Immune System. Comparing ABMs and Gillespie’s Stochastic Simulation Algorithm results we show that our methodology brings coherence among the results of ABMs and SSA.

High-level Petri nets (HLPNs) are an expressive formalism well supported by a number of tools that automate the editing and the interactive simulation of models and some kinds of analytical techniques, mainly based on state-space exploration. Structural analysis of HLPNs is, however, a challenging task not yet adequately supported and it is often accomplished via the unfolding of an HLPN into a corresponding low-level Petri Net. An approach to derive a system of Ordinary Differential Equations (ODEs) from a Stochastic Symmetric Net (SSN) has been proposed a few years ago, based on the net's unfolding and subsequent grouping of similar equations. This method has been recently improved by providing an algorithm that directly derives a compact ODE system (from a partially unfolded net) in a symbolic way, through algebraic manipulation of SSN annotations. In this paper, we present the automation of the calculus of Symbolic ODEs (SODEs) for SSN models as a new module of SNexpression, a tool for the symbolic structural analysis of Symmetric Nets. An application of the tool/technique to a variant of a SIRS epidemic model including antibiotic resistance is also described.

Stochastic Symmetric Nets (SSNs) are a High-Level Stochastic Petri Net formalism which provides a parametric system description and an efficient analysis technique that exploit system symmetries to automatically aggregate its states. Even if significant reductions can be achieved in highly symmetric models, the reduced state space can still be too large to derive and/or solve the underlying stochastic process, so that Monte Carlo simulation and fluid approximation remain the only viable ways that need to be explored. In this paper, we contribute to this line of research by proposing a new approach based on fluid approximation to automatically derive from an SSN model a set of ordinary differential equations (ODEs) which mimic the system behavior, and by showing how the SSN formalism allows us to define an efficient translation method which reduces the size of the corresponding ODE system with an automatic exploitation of system symmetries. Additionally, some case studies are presented to show the effectiveness of the method and the relevance of its application in practical cases.

In this paper, the Non-deterministic Repairable Fault Tree (NdRFT) formalism is proposed: it allows the modeling of failures of complex systems in addition to their repair processes. Its originality with respect to other Fault Tree extensions allows us to address repair strategy optimization problems: in an NdRFT model, the decision as to whether to start or not a given repair action is non-deterministic, so that all the possibilities are left open. The formalism is rather powerful, it allows: the specification of self-revealing events, the representation of components degradation, the choice among local repair, global repair, preventive maintenance, and the specification of the resources needed to start a repair action. The optimal repair strategy with respect to some relevant system state function, e.g. system unavailability, can then be computed by solving an optimization problem on a Markov Decision Process derived from the NdRFT. Such derivation is obtained by converting the NdRFT model into an intermediate formalism called Markov Decision Petri Net (MDPN). In the paper, the NdRFT syntax and semantics are formally described, together with the conversion rules to derive from the NdRFT the corresponding MDPN model. The application of NdRFT is illustrated through examples.

Computer Interpretable Guidelines (CIG) are an emerging area of research, to support medical decision making through evidence-based recommendations. However, new challenges in the data management field have to be faced, to integrate CIG management with a proper treatment of patient data, and of other forms of medical knowledge (e.g., causal and behavioral knowledge). In this position paper, we summarize a proposal for a research agenda that, in our opinion, can lead to a significant advancement in the field. The goal of the work is to provide suitable models and reasoning methodologies to cope with the aforementioned aspects, and to properly integrate them for medical decision support. Achieving such a goal requires advances in data management, and, in particular, in the treatment of indeterminate valid-time data in relational databases, of temporal abstraction on time series, of case retrieval on time series, of design-time and run-time model-based verification of guidelines, of case-based reasoning, of non-monotonic logics, of formal ontologies, of probabilistic graphical models (Bayesian Networks and Influence Diagrams).

The Hybrid Automata Stochastic Logic (HASL) has been recently defined as a flexible way to express classical performance measures as well as more complex, path-based ones (generically called "HASL formulas"). The considered paths are executions of Generalized Stochastic Petri Nets (GSPN), which are an extension of the basic Petri net formalism to define discrete event stochastic processes. The computation of the HASL formulas for a GSPN model is demanded to the COSMOS tool, that applies simulation techniques to the formula computation. Stochastic Symmetric Nets (SSN) are a high level Petri net formalism, of the colored type, in which tokens can have an identity, and it is well known that colored Petri nets allow one to describe systems in a more compact and parametric form than basic (uncolored) Petri nets. In this paper we propose to extend HASL and COSMOS to support colors, so that performance formulas for SSN can be easily defined and evaluated. This requires a new definition of the logic, to ensure that colors are taken into account in a correct and useful manner, and a significant extension of the COSMOS tool.

In this paper we propose an improved BDD approach to the network reliability analysis, that allows the user to compute an exact solution or an approximation based on reliability bounds when network complexity makes the former solution practically impossible. To this purpose, a new algorithm for encoding the connectivity graph on a Binary Decision Diagram (BDD) has been developed; it reduces the computation memory peak with respect to previous approaches based on the same type of data structure without increasing the execution time, and allows us also to derive from a subset of the minpaths/mincuts a lower/upper bound of the network reliability, so that the quality of the obtained approximation can be estimated. Finally, a fair and detailed comparison between our approach and another state of the art approach presented in the literature is documented through a set of benchmarks.

The increasing demand for customer centric evaluation of systems, mostly related with the assessment of the quality of service that they can deliver, requires the development of techniques properly designed to model and to study the movement of specific entities generically referred to as "customers". Stochastic Well-Formed Net(SWN) are naturally suited for the representation of systems in which "customers" of different categories compete for the use of common resources. Color classes of SWN are easily associated with these different categories, leaving to the peculiar features of the formalism the possibility of exploiting all the symmetries existing into the representation for the efficient and effective computation of the measures of interest. Within this application context, the computation of first passage time distribution measures in Stochastic Well-Formed Net (SWN) is becoming of primary interest. Customers however are not primitive entities in the formalism and an approach similar to that previously developed for Generalized Stochastic Petri Nets (GSPN) is suggested to overcome this problem in which P-semiflows are used to identify the circulating "customers". In this paper we propose an original algorithm for computing some P-semiflows of colored PNs (in particular of SWNs) in parametric form by exploiting the peculiarities of the objective of this investigation, and extend the customer centric first passage time computation approach previously developed for GSPNs, to make it suitable for SWN models. Moreover, the paper proposes an enhancement of the SWN notation in order to provide a way to ease the modeler in the specification of customer scheduling policies that may affect the computation of first passage time distributions. This extension, inspired by Queueing Petri Nets, adds to SWN some "syntactic sugar" that allows to include in the model queueing places which are automatically replaced by appropriate submodels, before solving the model.

This paper presents an extension of the generalized stochastic Petri net (GSPN) formalism that enables the computation of first passage time distributions. The tagged customer technique typical of queuing networks is adapted to the GSPN context by providing a formal definition and an automatic computation of the groups of tokens that can be identified as customers, i.e. classes of homogeneous entities behaving in a similar manner. Passage times are identified through the concept of events that correspond to the firing of transitions placed at the boundaries of a subnet. The extended model obtained with this specifications is translated into an ordinary GSPN by isolating a customer from the group and highlighting its path through the net thus obtaining a representation suited for the passage time analysis. Proofs are provided to show the equivalence between these models with respect to their steady-state distributions. An important and original aspect treated in this paper is the possibility of specifying several scheduling policies of tokens at places, an information not present in ordinary GSPN models, but that is vital for the precise computation of first passage time distributions as shown by a few results computed for a simple Flexible Manufacturing application.

This issue is dedicated to selected papers from the 30th International Conference on Applications and Theory of Petri Nets and Other Models of Concurrency which took place in June 2009 in Paris.For that conference, 19 regular contributions were selected among 46 submissions, in a careful reviewing process.

Non deterministic Repairable Fault Trees (NdRFT) are a recently proposed modeling formalism for the study of optimal repair strategies: they are based on the widely adopted Fault Tree formalism, but in addition to the failure modes, NdRFTs allow to define possible repair actions. In a previous pa per the formalism has been introduced together with an analysis method and a tool allowing to automatically derive the best repair strategy to be applied in each state. The analysis technique is based on the generation and solution of a Markov Decision Process. In this paper we present an extension, ParNdRFT, that allows to exploit the presence of redundancy to reduce the complexity of the model and of the analysis. It is based on the translation of the ParNdRFT in to a Markov Decision Well-Formed Net, i.e. a model specified by means of an High Level Petri Net formalism. The translated model can be efficiently solved thanks to existing algorithms that generate a reduced state space automatically exploiting the model symmetries.

The virtual reality simulator used in the e-DISTRICT CiPro Pilot-Project, in the context of the EUprogramme Leonardo da Vinci II, allows the realization of an Internet based Collaborative Virtual Environment and it can be used for Education and Training for Civil Protection Operators, and for Research purposes in new operational protocols and their effectiveness (which would be very expensive in case of real drills, and also very difficult to organize). In this article we focus on the goals of the e- District CiPro Project, and we present the virtual reality simulator. We also address the importance of the detail level of the scenarios and we consider their customization and their integration with other software that simulates the phase of treatment in hospital.

Fault trees are a well known mean for the evaluation of dependability of complex systems. Many extensions have been proposed to the original formalism in order to enhance the advantages of fault tree analysis for the design and assessment of systems. In this paper we propose an extension, repairable fault trees, which allows the designer to evaluate the effects of different repair policies on a repairable system: this extended formalism has been integrated in a multi-formalism multi-solution framework, and it is supported by a solution technique which transparently exploits generalized stochastic Petri nets (GSPN)for modelling the repairing process. The modelling technique and the solution process are illustrated through an example.

UNLABELLED: RRE allows the extraction of non-coding regions surrounding a coding sequence [i.e. gene upstream region, 5'-untranslated region (5'-UTR), introns, 3'-UTR, downstream region] from annotated genomic datasets available at NCBI. AVAILABILITY: RRE parser and web-based interface are accessible at http://www.bioinformatica.unito.it/bioinformatics/rre/rre.html

In order to cope efficiently with the dependability analysis of redundant systems with replicated units, a new, more compact fault-tree formalism, called Parametric Fault Tree (PFT), is defined. In a PFT formalism, replicated units are folded and indexed so that only one representative of the similar replicas is included in the model. From the PFT, a list of parametric cut sets can be derived, where only the relevant patterns leading to the system failure are evidenced regardless of the actual identity of the component in the cut set. The paper provides an algorithm to convert a PFT into a class of High-Level Petri Nets, called SWN. The purpose of this conversion is twofold: to exploit the modeling power and flexibility of the SWN formalism, allowing the analyst to include statistical dependencies that could not have been accommodated into the corresponding PFT and to exploit the capability of the SWN formalism to generate a lumped Markov chain, thus alleviating the state explosion problem. The search for the minimal cut sets (qualitative analysis) can be often performed by a structural T-invariant analysis on the generated SWN. The advantages that can be obtained from the translation of a PFT into a SWN are investigated considering a fault-tolerant multiprocessor system example.

Generalized stochastic Petri nets (GSPN), with immediate transitions, are extensively used to model concurrent systems in a wide range of application domains, particularly including software and hardware aspects of computer systems, and their interactions. These models are typically used for system specification, logical and performance analysis, or automatic code generation. In order to keep modeling separate from the analysis and to gain in efficiency and robustness of the modeling process, the complete specification of the stochastic process underlying a model should be guaranteed at the net level, without requiring the generation and exploration of the state space. In this paper, we propose a net-level method that guides the modeler in the task of defining the priorities (and weights) of immediate transitions in a GSPN model, to deal with confusion and conflict problems. The application of this method ensures well-definition without reducing modeling flexibility or expressiveness.

The DrawNet++ project addresses the compositional construction of dependability models. Its main goals are to provide: a) a GUI to any graph-based formalism; b) a support to the design process of dependability models, according to concepts inspired by object orientation (OO); c) a user friendly front-end for different classes of analysis/simulation tools.

Stochastic well formed colored nets (SWN) make it possible to apply performance evaluation techniques on a compact representation of the reachability graph (RG), called the Symbolic RG (SRG). The Extended SRG (ESRG) has been proposed to achieve a higher degree of reduction when applied to partially symmetric SWN models. The price to pay for the more effective state space reduction, is a loss of information on the paths of the graph. Despite this loss, it has been shown that several qualitative properties can be studied on the ESRG. We consider the possibility of using the ESRG for performance evaluation purposes and more specifically, we explore how the ergodicity of the system can be decided on this graph.

No abstract available.

Structural symmetries in stochastic well-formed colored Petri nets (SWN's) lead to behavioral symmetries that can be exploited by using the symbolic reachability graph (SRG) construction algorithm. The SRC allows one to compute an aggregated reachability graph (RG) and a "lumped" continuous time Markov chain (CTMC) that contain all the information needed to study the qualitative properties and the performance of the modeled system, respectively. Some models exhibit qualitative behavioral symmetries that are not completely reflected at the CTMC level. We call them quasi-lumpable SWN models. In these cases, exact performance indices can be obtained by avoiding the aggregation of those markings that are qualitatively, but not quantitatively, equivalent. An alternative approach consists of aggregating all the qualitatively equivalent states and computing approximated performance indices. In this paper, a technique is proposed to compute bounds on the performance of SWN models of this kind, using the results we have presented elsewhere. The technique is based on the Courtois and Semal bounded aggregation method.>

The class of stochastic well-formed colored nets (SWN's) was defined as a syntactic restriction of stochastic high-level nets. The interest of the introduction of restrictions in the model definition is the possibility of exploiting the symbolic reachability graph (SRG) to reduce the complexity of Markovian performance evaluation with respect to classical Petri net techniques. It turns out that SWN's allow the representation of any color function in a structured form, so that any unconstrained high-level net can be transformed into a well-formed net. Moreover, most constructs useful for the modeling of distributed computer systems and architectures directly match the "well-formed" restriction, without any need of transformation. A nontrivial example of the usefulness of the technique in the performance modeling and evaluation of multiprocessor architectures is included.>

A class of Petri nets in which transitions can fire either immediately or after an exponentially distributed random time since their enabling (GSPN), is used to define performance models for Flexible Manufacturing Systems. An example is discussed in which the analysis of the GSPN model yields results that are more reliable than those obtained with a currently used method based on queueing networks. Detailed GSPN models are shown that allow accurate analyses of the effects that different scheduling policies and pallet mixes have on system performance.