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.

ICT infrastructures are getting increasingly complex, and defending them against cyber attacks is cumbersome. As cyber threats continue to increase and expert resources are limited, organizations must find more efficient ways to evaluate their resilience and take proactive measures. Threat modeling is an excellent method of assessing the resilience of ICT systems, for example, by building Attack Graphs that illustrate an adversary's attack vectors. Previously, the Meta Attack Language (MAL) was proposed, which serves as a framework to develop Domain Specific Languages (DSLs) and generate Attack Graphs for modeled infrastructures. coreLang is a MAL-based threat modeling language that utilizes Attack Graphs to enable attack simulations and security assessments. In this work, we present the first release version of coreLang in which MITRE ATT&CK tactics and techniques are mapped onto to serve as a validation and identify strengths and weaknesses to benefit the development cycle. Our validation showed that coreLang does cover 46% of all the techniques included in the matrix, while if we additionally exclude the tactics that are intrinsically not covered by coreLang and MAL, the coverage percentage increases to 64%.

The complexity of ICT infrastructures is continuously increasing, presenting a formidable challenge in safeguarding them against cyber attacks. In light of escalating cyber threats and limited availability of expert resources, organizations must explore more efficient approaches to assess their resilience and undertake proactive measures. Threat modeling is an effective approach for assessing the cyber resilience of ICT systems. One method is to utilize Attack Graphs, which visually represent the steps taken by adversaries during an attack. Previously, MAL (the Meta Attack Language) was proposed, which serves as a framework for developing Domain-Specific Languages (DSLs) and generating Attack Graphs for modeled infrastructures. coreLang is a MAL-based threat modeling language that utilizes such Attack Graphs to enable attack simulations and security assessments for the generic ICT domain. Developing domain-specific languages for threat modeling and attack simulations provides a powerful approach for conducting security assessments of infrastructures. However, ensuring the correctness of these modeling languages raises a separate research question. In this study we conduct an empirical experiment aiming to falsify such a domain-specific threat modeling language. The potential inability to falsify the language through our empirical testing would lead to its corroboration, strengthening our belief in its validity within the parameters of our study. The outcomes of this approach indicated that, on average, the assessments generated by attack simulations outperformed those of human experts. Additionally, both human experts and simulations exhibited significantly superior performance compared to random guessers in their assessments. While specific human experts occasionally achieved better assessments for particular questions in the experiments, the efficiency of simulation-generated assessments surpasses that of human domain experts.