Analysis of Broadcast Authentication Mechanism in Selected Network Topologies

This paper deals with simulation of the broadcast authentication protocols using Colored Petri Nets and further optimizations in Matlab environment. Typical application of broadcast authentication protocols can be configurations where only one transmitter with multiple recipients exists (such as message exchange in sensor networks routing protocols, or the leader election process in sensors network). Authentication of every packet seems to be very effective way to mitigate an attack, however resulting in increase of end-to-end delay. To mitigate this drawback, the broadcast authentication protocols have been proposed. Concept of optimization of the broadcast authentication protocol DREAM parameters in a special case of fully N-ary tree and general random topology containing the same amount of nodes with regard to delay and energy consumption minimization is showed in the paper. Protocol DREAM was taken as an example of broadcast authenticating protocol to show how Color Petri Nets can be used to create a fully functional model of the protocol

Dependability Analysis of Control Systems using SystemC and Statistical Model Checking

Stochastic Petri nets are commonly used for modeling distributed systems in order to study their performance and dependability. This paper proposes a realization of stochastic Petri nets in SystemC for modeling large embedded control systems. Then statistical model checking is used to analyze the dependability of the constructed model. Our verification framework allows users to express a wide range of useful properties to be verified which is illustrated through a case study

Integration of a failure monitoring within a hybrid dynamic simulation environment

The complexity and the size of the industrial chemical processes induce the monitoring of a growing number of process variables. Their knowledge is generally based on the measurements of system variables and on the physico-chemical models of the process. Nevertheless this information is imprecise because of process and measurement noise. So the research ways aim at developing new and more powerful techniques for the detection of process fault. In this work, we present a method for the fault detection based on the comparison between the real system and the reference model evolution generated by the extended Kalman filter. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. It is a general object-oriented environment which provides common and reusable components designed for the development and the management of dynamic simulation of industrial systems. The use of this method is illustrated through a didactic example relating to the field of Chemical Process System Engineering

Modelling and simulation of a biometric identity-based cryptography

Government information is a vital asset that must be kept in a trusted environment and efficiently managed by authorised parties. Even though e-Government provides a number of advantages, it also introduces a range of new security risks. Sharing confidential and top-secret information in a secure manner among government sectors tend to be the main element that government agencies look for. Thus, developing an effective methodology is essential and it is a key factor for e-Government success. The proposed e-Government scheme in this paper is a combination of identity-based encryption and biometric technology. This new scheme can effectively improve the security in authentication systems, which provides a reliable identity with a high degree of assurance. In addition, this paper demonstrates the feasibility of using Finite-state machines as a formal method to analyse the proposed protocols

Learning Petri net models of non-linear gene interactions

Understanding how an individual's genetic make-up influences their risk of disease is a problem of paramount importance. Although machine-learning techniques are able to uncover the relationships between genotype and disease, the problem of automatically building the best biochemical model or “explanation” of the relationship has received less attention. In this paper, I describe a method based on random hill climbing that automatically builds Petri net models of non-linear (or multi-factorial) disease-causing gene–gene interactions. Petri nets are a suitable formalism for this problem, because they are used to model concurrent, dynamic processes analogous to biochemical reaction networks. I show that this method is routinely able to identify perfect Petri net models for three disease-causing gene–gene interactions recently reported in the literature

Performance modeling of e-procurement workflow using Generalised Stochastic Petri net (GSPN)

This paper proposes a Generalised Stochastic Petri net (GSPN) model representing a generic e-procurement workflow process. The model displays the dynamic behaviour of the system and shows the inter relationship of process activities. An analysis based on matrix equation approach enabled users to analyse the critical system's states, and thus justify the process performance. The results obtained allow users for better decision making in improving e-procurement workflow performance

Abridged Petri Nets

A new graphical framework, Abridged Petri Nets (APNs) is introduced for bottom-up modeling of complex stochastic systems. APNs are similar to Stochastic Petri Nets (SPNs) in as much as they both rely on component-based representation of system state space, in contrast to Markov chains that explicitly model the states of an entire system. In both frameworks, so-called tokens (denoted as small circles) represent individual entities comprising the system; however, SPN graphs contain two distinct types of nodes (called places and transitions) with transitions serving the purpose of routing tokens among places. As a result, a pair of place nodes in SPNs can be linked to each other only via a transient stop, a transition node. In contrast, APN graphs link place nodes directly by arcs (transitions), similar to state space diagrams for Markov chains, and separate transition nodes are not needed. Tokens in APN are distinct and have labels that can assume both discrete values ("colors") and continuous values ("ages"), both of which can change during simulation. Component interactions are modeled in APNs using triggers, which are either inhibitors or enablers (the inhibitors' opposites). Hierarchical construction of APNs rely on using stacks (layers) of submodels with automatically matching color policies. As a result, APNs provide at least the same modeling power as SPNs, but, as demonstrated by means of several examples, the resulting models are often more compact and transparent, therefore facilitating more efficient performance evaluation of complex systems.Comment: 17 figure

Stochastic maintenance models for ceramic claddings

The authors gratefully acknowledge the support of the CERIS Research Institute, IST, University of Lisbon and the FCT (Foundation for Science and Technology), through the projects SLPforBMS (PTDC/ECM‐COM/5772/2014) and Best Maintenance-Lower Risks (PTDC/ECI-CON/29286/2017).Maintenance decision-making involves a series of multiple objectives, some of them contradictory. Usually, stakeholders intend to find the optimal maintenance strategy, to minimize the economic burden, while simultaneously maximizing the buildings’ performance. In this study, a condition-based maintenance model, based on Petri nets, is proposed to evaluate the consequences of alternative maintenance strategies to maintain and improve the performance of ceramic claddings. This maintenance model is a full life-cycle model that integrates the stochastic assessment of the degradation condition of the claddings, and also inspections, maintenance and renewal processes. Three maintenance strategies are considered: (i) major intervention only; (ii) combination of minor and major interventions; and (iii) combination of cleaning operations, minor and major interventions. The uncertainties associated with the degradation process, as well as with the definition of the effects of maintenance actions are considered by modelling the transitions times in Petri nets as random variables. Considering the complexity of Petri nets, the statistical descriptors of the performance of the assets (e.g., mean condition, probability of applying maintenance) were computed using Monte Carlo simulation. The impact of the different maintenance strategies in the claddings’ service life is discussed, comparing the different alternatives also from an economic point of view.publishersversionpublishe

Bisimulation Relations Between Automata, Stochastic Differential Equations and Petri Nets

Two formal stochastic models are said to be bisimilar if their solutions as a stochastic process are probabilistically equivalent. Bisimilarity between two stochastic model formalisms means that the strengths of one stochastic model formalism can be used by the other stochastic model formalism. The aim of this paper is to explain bisimilarity relations between stochastic hybrid automata, stochastic differential equations on hybrid space and stochastic hybrid Petri nets. These bisimilarity relations make it possible to combine the formal verification power of automata with the analysis power of stochastic differential equations and the compositional specification power of Petri nets. The relations and their combined strengths are illustrated for an air traffic example.Comment: 15 pages, 4 figures, Workshop on Formal Methods for Aerospace (FMA), EPTCS 20m 201