IT has evolved over the decades, where its role and impact have transitioned from being a tactical tool to a more strategic one for driving business strategies to transform organizations. The right alignment between IT strategy and business has become a compelling factor for Chief Information Officers and Enterprise Architecture (EA) in practice is one of the approaches where this alignment can be achieved. Enterprise Modeling complements EA with models that are composed of enterprise components and relationships, that are stored in a repository. Over time, the repository grows which opens up research avenues to provide data intelligence. Recommender Systems is a field that can take different forms in the modeling domain and each form of recommendation can be enhanced with sophisticated models over time. Within this work, we focus on the latter problem by providing a recommender architecture framework eases the integration of different Recommender Systems. Thus, researchers can easily compare the performance of different recommender systems for EA models. The framework is developed as a distributed plugin for Archi, a widely used modeling tool to create EA models in the ArchiMate notation.

IT systems pervade our society more and more, and we become heavily dependent on them. At the same time, these systems are increasingly targeted in cyberattacks, making us vulnerable. Enterprise and cybersecurity responsibles face the problem of defining techniques that raise the level of security. They need to decide which mechanism provides the most efficient defense with limited resources. Basically, the risks need to be assessed to determine the best cost-to-benefit ratio. One way to achieve this is through threat modeling; however, threat modeling is not commonly used in the enterprise IT risk domain. Furthermore, the existing threat modeling methods have shortcomings. This paper introduces a metamodel-based approach named Yet Another Cybersecurity Risk Assessment Framework (Yacraf). Yacraf aims to enable comprehensive risk assessment for organizations with more decision support. The paper includes a risk calculation formalization and also an example showing how an organization can use and benefit from Yacraf.

Successfully developing domain-specific languages (DSLs) demands language engineers to consider their organizational context, which is challenging. Action design research (ADR) provides a conceptual framework to address this challenge. Since ADR’s application to the engineering of DSLs has not yet been examined, we investigate applying it to the development of threat modeling DSLs based on the Meta Attack Language (MAL), a metamodeling language for the specification of domain-specific threat modeling languages. To this end, we conducted a survey with experienced MAL developers on their development activities. We extract guidelines and align these, together with established DSL design guidelines, to the conceptual model of ADR. The research presented, aims to be the first step to investigate whether ADR can be used to systematically engineer DSLs.

Attack simulations are a feasible means of assessing the cyber security of various systems. Simulations can replicate the steps taken by an attacker to compromise sensitive system assets, and the time required for the acquisition of assets of interests can be calculated. One widely accepted approach to such simulations is the modelling of attack steps and their dependencies in a formal manner using attack graphs. To reduce the effort of creating new attack graphs for each system in a given domain, one can employ domain-specific attack-modeling languages to codify common attack logic. The Meta Attack Language has been proposed as a framework for developing domain-specific attack languages. In this article, we propose vehicleLang as a domain-specific language for modeling vehicles in the context of corresponding information technology infrastructures and analyzing weaknesses related to known attacks. To model domain-specific attributes, we reviewed existing literature to develop a comprehensive language, which was then verified through a series of interviews with domain experts from the automotive industry. Specifically, a systematic literature review was performed to identify possible attacks against vehicles. The identified attacks served as a blueprint for the evaluation of vehicleLang's simulation capabilities. Finally, the language was validated using the Feigenbaum test methodology.

Cyber attacks on IT and OT systems can have severe consequences for individuals and organizations, from water or energy distribution systems to online banking services. To respond to these threats, attack simulations can be used to assess the cyber security of systems to foster a higher degree of resilience against cyber attacks; the steps taken by an attacker to compromise sensitive system assets can be traced, and a time estimate can be computed from the initial step to the compromise of assets of interest.

Previously, the Meta Attack Language (MAL) was introduced as a framework to develop security-oriented domain-specific languages. It allows attack simulations on modeled systems and analyzes weaknesses related to known attacks. To produce more realistic simulation results, probability distributions can be assigned to attack steps and defenses to describe the efforts required for attackers to exploit certain attack steps. However, research on assessing such probability distributions is scarce, and we often rely on security experts to model attackers’ efforts. To address this gap, we propose a method to assign probability distributions to the attack steps and defenses of MAL-based languages. We demonstrate the proposed method by assigning probability distributions to a MAL-based language. Finally, the resulting language is evaluated by modeling and simulating a known cyber attack.

Enterprise systems are growing in complexity, and the adoption of cloud and mobile services has greatly increased the attack surface. To proactively address these security issues in enterprise systems, a threat modeling language for enterprise systems called enterpriseLang was proposed. It is a domain-specific language (DSL) designed using the Meta Attack Language (MAL) framework and focuses on describing system assets, attack steps, defenses, and asset associations. The threat models can serve as input for attack simulations to analyze the behavior of attackers within the system. However, whether and to what extent the functionality of these threat modeling languages is achieved has not been addressed. To ensure the correct functionality of threat modeling languages, this paper proposes a method to assess the quality of such languages and illustrates its application using enterpriseLang.

Enterprise systems are growing in complexity, and the adoption of cloud and mobile services has greatly increased the attack surface. To proactively address these security issues in enterprise systems, a threat modeling language for enterprise systems called enterpriseLang was proposed. It is a domain-specific language (DSL) designed using the Meta Attack Language (MAL) framework and focuses on describing system assets, attack steps, defenses, and asset associations. The threat models can serve as input for attack simulations to analyze the behavior of attackers within the system. However, whether and to what extent the functionality of these threat modeling languages is achieved has not been addressed. To ensure the correct functionality of threat modeling languages, this paper proposes a method to assess the quality of such languages and illustrates its application using enterpriseLang.

The increase of cyber-attacks raised security concerns for critical assets worldwide in the last decade. Leading to more efforts spent towards increasing the cyber security among companies and countries. For the sake of enhancing cyber security, representation and testing of attacks have prime importance in understanding system vulnerabilities. One of the available tools for simulating attacks on systems is the Meta Attack Language (MAL), which allows representing the effects of certain cyber-attacks. However, only understanding the component vulnerabilities is not enough in securing enterprise systems. Another important factor is the "human", which constitutes the biggest "insider threat". For this, Security Behavior Analysis (SBA) helps understanding which system components that might be directly affected by the "human". As such, in this work, the authors present an approach for integrating user actions, so called "security behavior", by mapping SBA to a MAL-based language through MITRE ATT&CK techniques.

The Enterprise Architecture (EA) discipline evolved during the past two decades and is now established in a large number of companies. Architectures in these companies changed over time and are now the result of a long creation and maintenance process. Such architectures still contain processes and services provided by legacy IT systems (e.g., systems, applications) that were reasonable during the time they were created but might now hamper the introduction of better solutions. In order to support handling those legacies, research on the notion of EA debts has been started. The concept of EA debts widens the scope of technical debts to cover also organizational aspects offering a mean for managing EA in dynamic environments. The research encompasses the development of methods for managing debts together with a repository of typical EA debts. Identifying EA debts for the repository is challenging as required knowledge is usually not documented. Therefore, a structured approach is needed to externalize this knowledge. The paper presents a workshop format that is used to identify EA debts in organizations. Corresponding workshops are performed in two distinct companies to support them in understanding certain issues they face. First results from those workshops are presented in the second part of the paper.