Statistical Model Checking of Dynamic Networks of Stochastic Hybrid Automata

In this paper we present a modelling formalism for dynamic networksof stochastic hybrid automata. In particular, our formalism is based on primitivesfor the dynamic creation and termination of hybrid automata components duringthe execution of a system. In this way we allow for natural modelling of conceptssuch as multiple threads found in various programming paradigms, as well as thedynamic evolution of biological systems.We provide a natural stochastic semantics of the modelling formalism based on re-peated output races between the dynamic evolving components of a system. Asspecification language we present a quantified extension of the logic Metric Tempo-ral Logic (MTL). As a main contribution of this paper, the statistical model checkingengine of U PPAAL has been extended to the setting of dynamic networks of hybridsystems and quantified MTL. We demonstrate the usefulness of the extended for-malisms in an analysis of a dynamic version of the well-known Train Gate example,as well as in natural monitoring of a MTL formula, where observations may lead todynamic creation of monitors for sub-formulas

Runtime Verification of Biological Systems

International audienceComplex computational systems are ubiquitous and their study increasingly important. Given the ease with which it is possible to construct large systems with heterogeneous technology, there is strong motivation to provide automated means to verify their safety, efficiency and reliability. In another context, biological systems are supreme examples of complex systems for which there are no design specifications. In both cases it is usually difficult to reason at the level of the description of the systems and much more convenient to investigate properties of their executions. To demonstrate runtime verification of complex systems we apply statistical model checking techniques to a model of robust biological oscillations taken from the literature. The model demonstrates some of the mechanisms used by biological systems to maintain reliable performance in the face of inherent stochasticity and is therefore instructive. To perform our investigation we use two recently developed SMC platforms: that incorporated in Uppaal and Plasma. Uppaalsmc offers a generic modeling language based on stochastic hybrid automata, while Plasma aims at domain specific support with the facility to accept biological models represented in chemical syntax

Statistical Model Checking for Stochastic Hybrid Systems

This paper presents novel extensions and applications of the UPPAAL-SMC model checker. The extensions allow for statistical model checking of stochastic hybrid systems. We show how our race-based stochastic semantics extends to networks of hybrid systems, and indicate the integration technique applied for implementing this semantics in the UPPAAL-SMC simulation engine. We report on two applications of the resulting tool-set coming from systems biology and energy aware buildings.Comment: In Proceedings HSB 2012, arXiv:1208.315

Refinement of Systems with an Attacker Focus

Tools and techniques for assessing the possibilities and impacts of attacks on IT systems are necessary to ensure the IT systems upon which society depends on continue to operate despite targeted attacks. This reality compels the development of intuitive brainstorming formalisms like attack-defense trees. With an attack-defense tree and a suitable system description, one can validate if a system succumbs to or withstands a described attack. Yet having established a secure system, it is still necessary to understand if and how system security may or may not be compromised or improved when the system requires modifications. Our research describes how we develop and implement a modeling methodology to resolve attacker-oriented refinement between systems.</p