Practical Applications of Probabilistic Model Checking to Communication Protocols (original) (raw)
Probabilistic Model Checking of the IEEE 802.11 Wireless Local Area Network Protocol
2002
The international standard IEEE 802.11 was developed recently in recognition of the increased demand for wireless local area networks. Its medium access control mechanism is described according to a variant of the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) scheme. Although collisions cannot always be prevented, randomised exponential backoff rules are used in the retransmission scheme to minimise the likelihood of repeated collisions. More precisely, the backoff procedure involves a uniform probabilistic choice of an integer-valued delay from an interval, where the size of the interval grows exponentially with regard to the number of retransmissions of the current data packet. We model the two-way handshake mechanism of the IEEE 802.11 standard with a fixed network topology using probabilistic timed automata, a formal description mechanism in which both nondeterministic choice and probabilistic choice can be represented. From our probabilistic timed automaton model, we obtain a finite-state Markov decision process via a property-preserving discrete-time semantics. The Markov decision process is then verified using Prism, a probabilistic model checking tool, against probabilistic, timed properties such as “at most 5,000 microseconds pass before a station sends its packet correctly.”
PRISM 4.0: Verification of probabilistic real-time systems
2011
This paper describes a major new release of the PRISM probabilistic model checker, adding, in particular, quantitative verification of (priced) probabilistic timed automata. These model systems exhibiting probabilistic, nondeterministic and real-time characteristics. In many application domains, all three aspects are essential; this includes, for example, embedded controllers in automotive or avionic systems, wireless communication protocols such as Bluetooth or Zigbee, and randomised security protocols. PRISM, which is open-source, also contains several new components that are of independent use. These include: an extensible toolkit for building, verifying and refining abstractions of probabilistic models; an explicit-state probabilistic model checking library; a discrete-event simulation engine for statistical model checking; support for generation of optimal adversaries/strategies; and a benchmark suite.
Symbolic Model Checking for Probabilistic Timed Automata
2004
Probabilistic timed automata are timed automata extended with discrete probability distributions, and can be used to model timed randomised protocols or faulttolerant systems. We present symbolic model-checking algorithms for probabilistic timed automata to verify both qualitative temporal logic properties, corresponding to satisfaction with probability 0 or 1, and quantitative properties, corresponding to satisfaction with arbitrary probability. The algorithms operate on zones, which represent sets of valuations of the probabilistic timed automaton's clocks. Our method considers only those system behaviours which guarantee the divergence of time with probability 1. The paper presents a symbolic framework for the verification of probabilistic timed automata against the probabilistic, timed temporal logic PTCTL. We also report on a prototype implementation of the algorithms using Difference Bound Matrices, and present the results of its application to the CSMA/CD and FireWire root contention protocol case studies.
Computer Science, 2022
Event-B is a formal method that is used in the development of safety-critical systems; however, these systems may introduce uncertainty and also need to meet real-time requirements, which make the modeling and analysis of such systems a challenging task. While some works exist that try to extend Event-B with probability and over time, they fail to address both in a single framework. Besides, these works mainly addressed extending the language itself, not integrating extended Event-B with verification. In this paper, we aim to represent both probability and time in the Event-B language, and we will show how such a representation can be automatically translated into the probabilistic timed automata (PTA) that are described in the language of the PRISM probabilistic model checker. This transformation approach would allow us to analyze the probabilistic and time-bounded probabilistic reachability properties of probabilistic real-time systems through probabilistic timed CTL (PTCTL) logic.
Modeling and Analysis of Probabilistic Timed Systems
2009
Abstract Probabilistic models are useful for analyzing systems which operate under the presence of uncertainty. In this paper, we present a technique for verifying safety and liveness properties for probabilistic timed automata. The proposed technique is an extension of a technique used to verify stochastic hybrid automata using an approximation with Markov Decision Processes. A case study for CSMA/CD protocol has been used to show case the methodology used in our technique.
2016
Using probabilities in the formal-methods-based development of safety-critical software has quick-ened interests in academia and industry. We address this area by our model-driven engineering method for reactive systems SPACE and its tool-set Reactive Blocks that provide an extension to support the modeling and verification of real-time behaviors. The approach facilitates the compo-sition of system models from reusable building blocks as well as the verification of functional and real-time properties and the automatic generation of Java code. In this paper, we describe the extension of the tool-set to enable the modeling and verification of probabilistic real-time system behavior with the focus on spatial properties that ensure system safety. In particular, we incorporate descriptions of probabilistic behavior into our Reactive Blocks models and integrate the model checker PRISM which allows to verify that a real-time system satis-fies certain safety properties with a given probabil...
Implementation of Symbolic Model Checking for Probabilistic Systems
2002
In this thesis, we present efficient implementation techniques for probabilistic model checking, a method which can be used to analyse probabilistic systems such as randomised distributed algorithms, fault-tolerant processes and communication networks. A probabilistic model checker inputs a probabilistic model and a specification, such as "the message will be delivered with probability 1", "the probability of shutdown occurring is at most 0.02" or "the probability of a leader being elected within 5 rounds is at least 0.98", and can automatically verify if the specification is true in the model.
PRISM: probabilistic model checking for performance and reliability analysis
Sigmetrics Performance Evaluation Review, 2009
Probabilistic model checking is a formal verification technique for the modelling and analysis of stochastic systems. It has proved to be useful for studying a wide range of quantitative properties of models taken from many different application domains. This includes, for example, performance and reliability properties of computer and communication systems. In this paper, we give an overview of the probabilistic model checking tool PRISM, focusing in particular on its support for continuous-time Markov chains and Markov reward models, and how these can be used to analyse performability properties.
Statistical probabilistic model checking with a focus on time-bounded properties
Information and Computation, 2006
Probabilistic verification of continuous-time stochastic processes has received increasing attention in the model-checking community in the past 5 years, with a clear focus on developing numerical solution methods for model checking of continuous-time Markov chains. Numerical techniques tend to scale poorly with an increase in the size of the model (the "state space explosion problem"), however, and are feasible only for restricted classes of stochastic discrete-event systems. We present a statistical approach to probabilistic model checking, employing hypothesis testing and discrete-event simulation. Since we rely on statistical hypothesis testing, we cannot guarantee that the verification result is correct, but we can at least bound the probability of generating an incorrect answer to a verification problem.
$$\mathcal {S}$$BIP 2.0: Statistical Model Checking Stochastic Real-Time Systems
Lecture Notes in Computer Science, 2018
This paper presents a major new release of SBIP, an extensible statistical model checker for Metric (MTL) and Linear-time Temporal Logic (LTL) properties on respectively Generalized Semi-Markov Processes (GSMP), Continuous-Time (CTMC) and Discrete-Time Markov Chain (DTMC) models. The newly added support for MTL, GSMPs, CTMCs and rare events allows to capture both real-time and stochastic aspects, allowing faithful specification, modeling and analysis of real-life systems. SBIP is redesigned as an IDE providing project management, model edition, compilation, simulation, and statistical analysis.