A Layered Reference Architecture for Metamodels to Tailor Quality Modeling and Analysis

Heinrich, Robert; Strittmatter, Misha; Reussner, Ralf

doi:10.1109/tse.2019.2903797

Cited by 10 publications

(3 citation statements)

References 53 publications

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“…Previous threat modeling work has involved domains that are primarily based on model-driven architecture modeling. Architecture modeling can help systems deal with increasingly complex cyber environments [33][34][35][36]. The importance of creating models to support security decisions has been previously demonstrated in some studies, including HinCTI [37], TV-HARM [38], and PMCAP [39], also employed architectural modeling, in which attacks and defenses were coupled to the system architecture.…”

Section: Experimental Methods Comparisonmentioning

confidence: 99%

Design of Threat Response Modeling Language for Attacker Profile Based on Probability Distribution

Zhang

Wang

Bai

et al. 2022

Wireless Communications and Mobile Computing

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Threat modeling and simulation (TMS) was aimed at dynamically capturing the features of attacks, which is a challenging job in complex Industrial Internet of Things (IIoT) control systems due to the complicated relationships among attacks. Recently, Meta Attack Language (MAL) showed its powerful TMS capabilities for representing complex attacks. However, existing methods pay less attention to the impact of changes in threat profiles on the simulation of key attack techniques. This paper proposes a novel method called threat response modeling language (TRMLang) for threat modeling and simulation in complex IIoT attacks. TRMLang obtains attacker information through an automated analysis of cyber threat intelligence (CTI) to build dynamic attacker profiles. Furthermore, it merges attacker features and probabilistic attack graphs in the simulation to improve TMS performance. The experimental results demonstrate that TRMLang can represent and evaluate the security conditions of IIoT control systems with two attack cases by Lazarus Group on SEGRID smart grids.

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Section: Experimental Methods Comparisonmentioning

confidence: 99%

Design of Threat Response Modeling Language for Attacker Profile Based on Probability Distribution

Zhang

Wang

Bai

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

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“…We also extend all relevant elements which contain some kind of condition to handle values of state variables in addition to parameter values. We extend the PCM with our required information by applying the inheritance and plain referencing extension approach, described by Heinrich et al [14]. We provide a separate metamodel with classes extending the existing metaclasses of the PCM, e.g., the getter and setter accesses, or elements which reference an existing element, such as the assignment of state variables to components.…”

Section: Integration Of Coverage Regions In the Architectural Modelmentioning

confidence: 99%

A Refactoring for Data Minimisation Using Formal Verification

Lanzinger

Ulbrich

Weigl

2022

Leveraging Applications of Formal Methods, Verification and Validation. Software Engineering

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Most formal methods see the correctness of a software system as a binary decision. However, proving the correctness of complex systems completely is difficult because they are composed of multiple components, usage scenarios, and environments. We present Quac, a modular approach for quantifying the correctness of service-oriented software systems by combining software architecture modeling with deductive verification. Our approach is based on a model of the service-oriented architecture and the probabilistic usage scenarios of the system. The correctness of a single service is approximated by a coverage region, which is a formula describing which inputs for that service are proven to not lead to an erroneous execution. The coverage regions can be determined by a combination of various analyses, e.g., formal verification, expert estimations, or testing. The coverage regions and the software model are then combined into a probabilistic program. From this, we can compute the probability that under a given usage profile no service is called outside its coverage region. We also present an implementation of Quac for Java using the modeling tool Palladio and the deductive verification tool KeY. We demonstrate its usability by applying it to a software simulation of an energy system. CCS CONCEPTS• Software and its engineering → Formal software verification; Object oriented architectures.

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“…Language workbenches [24] span technological spaces by providing and combining multiple formalisms, such as grammars and template languages for code generation [66] or reference architectures for metamodels [34] and interpreters for model execution [76] to support engineering multiple aspects of modelling languages. Such workbenches come with powerful tools and documented workflows describing how to engineer languages with the given formalisms.…”

Section: Composition Of Modelling Languagesmentioning

confidence: 99%

Multi-paradigm modelling for cyber–physical systems: a descriptive framework

et al. 2021

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The complexity of cyber–physical systems (CPSs) is commonly addressed through complex workflows, involving models in a plethora of different formalisms, each with their own methods, techniques, and tools. Some workflow patterns, combined with particular types of formalisms and operations on models in these formalisms, are used successfully in engineering practice. To identify and reuse them, we refer to these combinations of workflow and formalism patterns as modelling paradigms. This paper proposes a unifying (Descriptive) Framework to describe these paradigms, as well as their combinations. This work is set in the context of Multi-Paradigm Modelling (MPM), which is based on the principle to model every part and aspect of a system explicitly, at the most appropriate level(s) of abstraction, using the most appropriate modelling formalism(s) and workflows. The purpose of the Descriptive Framework presented in this paper is to serve as a basis to reason about these formalisms, workflows, and their combinations. One crucial part of the framework is the ability to capture the structural essence of a paradigm through the concept of a paradigmatic structure. This is illustrated informally by means of two example paradigms commonly used in CPS: Discrete Event Dynamic Systems and Synchronous Data Flow. The presented framework also identifies the need to establish whether a paradigm candidate follows, or qualifies as, a (given) paradigm. To illustrate the ability of the framework to support combining paradigms, the paper shows examples of both workflow and formalism combinations. The presented framework is intended as a basis for characterisation and classification of paradigms, as a starting point for a rigorous formalisation of the framework (allowing formal analyses), and as a foundation for MPM tool development.

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A Layered Reference Architecture for Metamodels to Tailor Quality Modeling and Analysis

Cited by 10 publications

References 53 publications

Design of Threat Response Modeling Language for Attacker Profile Based on Probability Distribution

Design of Threat Response Modeling Language for Attacker Profile Based on Probability Distribution

A Refactoring for Data Minimisation Using Formal Verification

Multi-paradigm modelling for cyber–physical systems: a descriptive framework

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