An Automated Quantitative Information Flow Analysis for Concurrent Programs

Salehi, Khayyam; Noroozi, Ali A.; Amir-Mohammadian, Sepehr; Mohagheghi, Mohammadsadegh

doi:10.1007/978-3-031-16336-4_3

Cited by 2 publications

(3 citation statements)

References 27 publications

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“…Besides, linear time properties are also covered in the specification step of the underlying system. Examples of the application of MDPs in this field includes the verification of hardware systems [2,3], computer networks [4,5], cyberphysical and cyber-security systems [6][7][8], medical sciences [9] robotics and software systems [10][11][12][13]. In reinforcement learning, the main focus is to approximate the optimal expected reward (or cost) before reaching a final state [14].…”

Section: Introductionmentioning

confidence: 99%

State ordering and classification for analyzing non-sparse large Markov Models

Mohagheghi

2023

Preprint

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Markov chains and Markov decision processes have been widely used to model the behavior of computer systems with probabilistic aspects. Numerical and iterative methods are commonly used to analyze these models. Many efforts have been made in recent decades to improve the efficiency of these numerical methods. In this paper, focusing on Markov models with non-sparse structure, a new set of heuristics is proposed for prioritizing model states with the aim of reducing the total computation time. In these heuristics, a set of simulation runs are used for statistical analysis of the effect of each state on the required values of the other states. Under this criterion, the priority of each state in updating its values is determined. The proposed heuristics provide a state ordering that improves the value propagation among the states. The proposed methods are also extended for very large models where disk-based techniques are required to analyze the models. Experimental results show that our proposed methods in this paper reduce the running times of the iterative methods for most cases of non-sparse models.

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Section: Introductionmentioning

confidence: 99%

State ordering and classification for analyzing non-sparse large Markov Models

Mohagheghi

2023

Preprint

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“…Bisimulation minimization is one of the model reduction techniques [3]. This approach can be applied to the verification of security protocols [1,35,38]. It defines an equivalence class on the model state space that can be applied to it.…”

Section: Introductionmentioning

confidence: 99%

“…One of the techniques used for model reduction is bisimulation minimization, which establishes an equivalence class on the state space of the model [2,26]. States within each equivalence class, known as bisimilar states, share the same set of properties.…”

Section: Introductionmentioning

confidence: 99%

Improving modified policy iteration for probabilistic model checking

Mohagheghi¹,

Karimpour

2022

csci

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Value iteration, policy iteration and their modified versions are well-known algorithms for probabilistic model checking of Markov Decision Processes. One the challenge of these methods is that they are time-consuming in most cases. Several techniques have been proposed to improve the performance of iterative methods for probabilistic model checking. However, the running time of these techniques depends on the graphical structure of the model and in some cases their performance is worse than the performance of the standard methods. In this paper, we propose two new heuristics to accelerate the modified policy iteration method. We first define a criterion for the usefulness of the computations of each iteration of this method. The first contribution of our work is to develop and use a criterion to reduce the number of iterations in modified policy iteration. As the second contribution, we propose a new approach to identify useless updates in each iteration. This method reduces the running time of computations by avoiding useless updates of states. The proposed heuristics have been implemented in the PRISM model checker and applied on several standard case studies. We compare the running time of our heuristics with the running time of previous standard and improved methods. Experimental results show that our techniques yields a significant speed-up.

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An Automated Quantitative Information Flow Analysis for Concurrent Programs

Cited by 2 publications

References 27 publications

State ordering and classification for analyzing non-sparse large Markov Models

State ordering and classification for analyzing non-sparse large Markov Models

Improving modified policy iteration for probabilistic model checking

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