We launch the new probabilistic model checker Storm. It features the analysis of discrete- and continuous-time variants of both Markov chains and MDPs. It supports the Prism and JANI modeling languages, probabilistic programs, dynamic fault trees and generalized stochastic Petri nets. It has a modular set-up in which solvers and symbolic engines can easily be exchanged. It offers a Python API for rapid prototyping by encapsulating Storm’s fast and scalable algorithms. Experiments on a variety of benchmarks show its competitive performance.

A Storm is Coming: A Modern Probabilistic Model Checker

DAG-based attack and defense modeling: don’t miss the forest for the attack trees

I provide an overview of the Fault Tree Analysis method.I review different extensions of fault trees.A number of model-based dependability analysis approaches are reviewed.I outline the future outlook for model-based dependability analysis. Fault Tree Analysis (FTA) is a well-established and well-understood technique, widely used for dependability evaluation of a wide range of systems. Although many extensions of fault trees have been proposed, they suffer from a variety of shortcomings. In particular, even where software tool support exists, these analyses require a lot of manual effort. Over the past two decades, research has focused on simplifying dependability analysis by looking at how we can synthesise dependability information from system models automatically. This has led to the field of model-based dependability analysis (MBDA). Different tools and techniques have been developed as part of MBDA to automate the generation of dependability analysis artefacts such as fault trees. Firstly, this paper reviews the standard fault tree with its limitations. Secondly, different extensions of standard fault trees are reviewed. Thirdly, this paper reviews a number of prominent MBDA techniques where fault trees are used as a means for system dependability analysis and provides an insight into their working mechanism, applicability, strengths and challenges. Finally, the future outlook for MBDA is outlined, which includes the prospect of developing expert and intelligent systems for dependability analysis of complex open systems under the conditions of uncertainty.

https://hull-repository.worktribe.com/preview/582881/2018-02-04%2015186%20Kabir.pdf

An overview of fault tree analysis and its application in model based dependability analysis

This paper presents the current state of the art on attack and defense modeling approaches that are based on directed acyclic graphs (DAGs). DAGs allow for a hierarchical decomposition of complex scenarios into simple, easily understandable and quantifiable actions. Methods based on threat trees and Bayesian networks are two well-known approaches to security modeling. However there exist more than 30 DAG-based methodologies, each having different features and goals. The objective of this survey is to present a complete overview of graphical attack and defense modeling techniques based on DAGs. This consists of summarizing the existing methodologies, comparing their features and proposing a taxonomy of the described formalisms. This article also supports the selection of an adequate modeling technique depending on user requirements.

DAG-Based Attack and Defense Modeling: Don't Miss the Forest for the Attack Trees

We launch the new probabilistic model checker storm. It features the analysis of discrete- and continuous-time variants of both Markov chains and MDPs. It supports the PRISM and JANI modeling languages, probabilistic programs, dynamic fault trees and generalized stochastic Petri nets. It has a modular set-up in which solvers and symbolic engines can easily be exchanged. It offers a Python API for rapid prototyping by encapsulating storm's fast and scalable algorithms. Experiments on a variety of benchmarks show its competitive performance.

A storm is Coming: A Modern Probabilistic Model Checker

https://ris.utwente.nl/ws/files/13291952/FTA_overview.pdf

Fault tree analysis

Fault tree analysis (FTA) is a very prominent method to analyze the risks related to safety and economically critical assets, like power plants, airplanes, data centers and web shops. FTA methods comprise of a wide variety of modelling and analysis techniques, supported by a wide range of software tools. This paper surveys over 150 papers on fault tree analysis, providing an in-depth overview of the state-of-the-art in FTA. Concretely, we review standard fault trees, as well as extensions such as dynamic FT, repairable FT, and extended FT. For these models, we review both qualitative analysis methods, like cut sets and common cause failures, and quantitative techniques, including a wide variety of stochastic methods to compute failure probabilities. Numerous examples illustrate the various approaches, and tables present a quick overview of results.

Fault Tree Analysis : A survey of the state-of-the-art in modeling, analysis and tools

The success of a security attack crucially depends on the resources available to an attacker: time, budget, skill level, and risk appetite. Insight in these dependencies and the most vulnerable system parts is key to providing effective counter measures.

This paper considers attack trees, one of the most prominent security formalisms for threat analysis. We provide an effective way to compute the resources needed for a successful attack, as well as the associated attack paths. These paths provide the optimal ways, from the perspective of the attacker, to attack the system, and provide a ranking of the most vulnerable system parts.

By exploiting the priced timed automaton model checker Uppaal CORA, we realize important advantages over earlier attack tree analysis methods: we can handle more complex gates, temporal dependencies between attack steps, shared subtrees, and realistic, multi-parametric cost structures. Furthermore, due to its compositionality, our approach is flexible and easy to extend.

We illustrate our approach with several standard case studies from the literature, showing that our method agrees with existing analyses of these cases, and can incorporate additional data, leading to more informative results.

Quantitative attack tree analysis via priced timed automata

We present an extensive collection of quantitative models to facilitate the development, comparison, and benchmarking of new verification algorithms and tools. All models have a formal semantics in terms of extensions of Markov chains, are provided in the Jani format, and are documented by a comprehensive set of metadata. The collection is highly diverse: it includes established probabilistic verification and planning benchmarks, industrial case studies, models of biological systems, dynamic fault trees, and Petri net examples, all originally specified in a variety of modelling languages. It archives detailed tool performance data for each model, enabling immediate comparisons between tools and among tool versions over time. The collection is easy to access via a client-side web application at qcomp.org with powerful search and visualisation features. It can be extended via a Git-based submission process, and is openly accessible according to the terms of the CC-BY license.

/pdf/the-quantitative-verification-benchmark-set-4cnyfhfimp.pdf

The Quantitative Verification Benchmark Set

Quantitative formal models capture probabilistic behaviour, real-time aspects, or general continuous dynamics. A number of tools support their automatic analysis with respect to dependability or performance properties. QComp 2019 is the first, friendly competition among such tools. It focuses on stochastic formalisms from Markov chains to probabilistic timed automata specified in the Jani model exchange format, and on probabilistic reachability, expected-reward, and steady-state properties. QComp draws its benchmarks from the new Quantitative Verification Benchmark Set. Participating tools, which include probabilistic model checkers and planners as well as simulation-based tools, are evaluated in terms of performance, versatility, and usability. In this paper, we report on the challenges in setting up a quantitative verification competition, present the results of QComp 2019, summarise the lessons learned, and provide an outlook on the features of the next edition of QComp.

/pdf/the-2019-comparison-of-tools-for-the-analysis-of-1065n0fnxf.pdf

Enno Jozef Johannes Ruijters

Papers

Fault tree analysis

Fault Tree Analysis : A survey of the state-of-the-art in modeling, analysis and tools

Quantitative attack tree analysis via priced timed automata

The Quantitative Verification Benchmark Set

The 2019 Comparison of Tools for the Analysis of Quantitative Formal Models: (QComp 2019 Competition Report)