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

A Formal Approach based on Fuzzy Logic for the Specification of Component-Based Interactive Systems

Formal methods are widely recognized as a powerful engineering method for the specification, simulation, development, and verification of distributed interactive systems. However, most formal methods rely on a two-valued logic, and are therefore limited to the axioms of that logic: a specification is valid or invalid, component behavior is realizable or not, safety properties hold or are violated, systems are available or unavailable. Especially when the problem domain entails uncertainty, impreciseness, and vagueness, the appliance of such methods becomes a challenging task. In order to overcome the limitations resulting from the strict modus operandi of formal methods, the main objective of this work is to relax the boolean notion of formal specifications by using fuzzy logic. The present approach is based on Focus theory, a model-based and strictly formal method for componentbased interactive systems. The contribution of this work is twofold: i) we introduce a specification technique based on fuzzy logic which can be used on top of Focus to develop formal specifications in a qualitative fashion; ii) we partially extend Focus theory to a fuzzy one which allows the specification of fuzzy components and fuzzy interactions. While the former provides a methodology for approximating I/O behaviors under imprecision, the latter enables to capture a more quantitative view of specification properties such as realizability.Comment: In Proceedings FESCA 2015, arXiv:1503.0437

Application and Control Aware Communication Strategies for Transportation and Energy Cyber-Physical Systems

Cyber--Physical Systems (CPSs) are a generation of engineered systems in which computing, communication, and control components are tightly integrated. Some important application domains of CPS are transportation, energy, and medical systems. The dynamics of CPSs are complex, involving the stochastic nature of communication systems, discrete dynamics of computing systems, and continuous dynamics of control systems. The existence of communication between and among controllers of physical processes is one of the basic characteristics of CPSs. Under this situation, some fundamental questions are: 1) How does the network behavior (communication delay, packet loss, etc.) affect the stability of the system? 2) Under what conditions is a complex system stabilizable?;In cases where communication is a component of a control system, scalability of the system becomes a concern. Therefore, one of the first issues to consider is how information about a physical process should be communicated. For example, the timing for sampling and communication is one issue. The traditional approach is to sample the physical process periodically or at predetermined times. An alternative is to sample it when specific events occur. Event-based sampling requires continuous monitoring of the system to decide a sample needs to be communicated. The main contributions of this dissertation in energy cyber-physical system domain are designing and modeling of event-based (on-demand) communication mechanisms. We show that in the problem of tracking a dynamical system over a network, if message generation and communication have correlation with estimation error, the same performance as the periodic sampling and communication method can be reached using a significantly lower rate of data.;For more complex CPSs such as vehicle safety systems, additional considerations for the communication component are needed. Communication strategies that enable robust situational awareness are critical for the design of CPSs, in particular for transportation systems. In this dissertation, we utilize the recently introduced concept of model-based communication and propose a new communication strategy to address this need. Our approach to model behavior of remote vehicles mathematically is to describe the small-scale structure of the remote vehicle movement (e.g. braking, accelerating) by a set of dynamic models and represent the large-scale structure (e.g. free following, turning) by coupling these dynamic models together into a Markov chain. Assuming model-based communication approach, a novel stochastic model predictive method is proposed to achieve cruise control goals and investigate the effect of new methodology.;To evaluate the accuracy and robustness of a situational awareness methodology, it is essential to study the mutual effect of the components of a situational awareness subsystem, and their impact on the accuracy of situational awareness. The main components are estimation and networking processes. One possible approach in this task is to produce models that provide a clear view into the dynamics of these two components. These models should integrate continuous physical dynamics, expressed with ordinary differential equations, with the discrete behaviors of communication, expressed with finite automata or Markov chain. In this dissertation, a hybrid automata model is proposed to combine and model both networking and estimation components in a single framework and investigate their interactions.;In summary, contributions of this dissertation lie in designing and evaluating methods that utilize knowledge of the physical element of CPSs to optimize the behavior of communication subsystems. Employment of such methods yields significant overall system performance improvement without incurring additional communication deployment costs

Parallel statistical model checking for safety verification in smart grids

By using small computing devices deployed at user premises, Autonomous Demand Response (ADR) adapts users electricity consumption to given time-dependent electricity tariffs. This allows end-users to save on their electricity bill and Distribution System Operators to optimise (through suitable time-dependent tariffs) management of the electric grid by avoiding demand peaks. Unfortunately, even with ADR, users power consumption may deviate from the expected (minimum cost) one, e.g., because ADR devices fail to correctly forecast energy needs at user premises. As a result, the aggregated power demand may present undesirable peaks. In this paper we address such a problem by presenting methods and a software tool (APD-Analyser) implementing them, enabling Distribution System Operators to effectively verify that a given time-dependent electricity tariff achieves the desired goals even when end-users deviate from their expected behaviour. We show feasibility of the proposed approach through a realistic scenario from a medium voltage Danish distribution network

On the analysis of stochastic timed systems

The formal methods approach to develop reliable and efficient safety- or performance-critical systems is to construct mathematically precise models of such systems on which properties of interest, such as safety guarantees or performance requirements, can be verified automatically. In this thesis, we present techniques that extend the reach of exhaustive and statistical model checking to verify reachability and reward-based properties of compositional behavioural models that support quantitative aspects such as real time and randomised decisions. We present two techniques that allow sound statistical model checking for the nondeterministic-randomised model of Markov decision processes. We investigate the relationship between two different definitions of the model of probabilistic timed automata, as well as potential ways to apply statistical model checking. Stochastic timed automata allow nondeterministic choices as well as nondeterministic and stochastic delays, and we present the first exhaustive model checking algorithm that allows their analysis. All the approaches introduced in this thesis are implemented as part of the Modest Toolset, which supports the construction and verification of models specified in the formal modelling language Modest. We conclude by applying this language and toolset to study novel distributed control strategies for photovoltaic microgenerators

A review of modelling and verification approaches for computational biology

This paper reviews most frequently used computational modelling approaches and formal verification techniques in computational biology. The paper also compares a number of model checking tools and software suits used in analysing biological systems and biochemical networks and verifiying a wide range of biological properties

On specification-based cyber-attack detection in smart grids

The transformation of power grids into intelligent cyber-physical systems brings numerous benefits, but also significantly increases the surface for cyber-attacks, demanding appropriate countermeasures. However, the development, validation, and testing of data-driven countermeasures against cyber-attacks, such as machine learning-based detection approaches, lack important data from real-world cyber incidents. Unlike attack data from real-world cyber incidents, infrastructure knowledge and standards are accessible through expert and domain knowledge. Our proposed approach uses domain knowledge to define the behavior of a smart grid under non-attack conditions and detect attack patterns and anomalies. Using a graph-based specification formalism, we combine cross-domain knowledge that enables the generation of whitelisting rules not only for statically defined protocol fields but also for communication flows and technical operation boundaries. Finally, we evaluate our specification-based intrusion detection system against various attack scenarios and assess detection quality and performance. In particular, we investigate a data manipulation attack in a future-orientated use case of an IEC 60870-based SCADA system that controls distributed energy resources in the distribution grid. Our approach can detect severe data manipulation attacks with high accuracy in a timely and reliable manner