Cyber security is attracting worldwide attention. With attacks being more and more common and often successful, no one is spared today. Threat modeling is proposed as a solution for secure application development and system security evaluations. Its aim is to be more proactive and make it more difficult for attackers to accomplish their malicious intents. However, threat modeling is a domain that lacks common ground. What is threat modeling, and what is the state-of-the-art work in this field? To answer these questions, this article presents a review of threat modeling based on systematic queries in four leading scientific databases. This is the first systematic literature review on threat modeling to the best of our knowledge. 176 articles were assessed, and 54 of them were selected for further analysis. We identified three separate clusters: (1) articles making a contribution to threat modeling, e.g., introducing a new method, (2) articles using an existing threat modeling approach, and (3) introductory articles presenting work related to the threat modeling process. The three clusters were analyzed in terms of a set of criteria, for instance: Is the threat modeling approach graphical or formal? Is it focused on a specific attack type and application? Is the contribution validated empirically or theoretically? We observe from the results that, most threat modeling work remains to be done manually, and there is limited assurance of their validations. The results can be used for researchers and practitioners who want to know the state-of-the-art threat modeling methods, and future research directions are discussed.

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, this paper proposes a threat modeling language for enterprise security based on the MITRE Enterprise ATT&CK Matrix. It is designed using the Meta Attack Language framework and focuses on describing system assets, attack steps, defenses, and asset associations. The attack steps in the language represent adversary techniques as listed and described by MITRE. This entity-relationship model describes enterprise IT systems as a whole; by using available tools, the proposed language enables attack simulations on its system model instances. These simulations can be used to investigate security settings and architectural changes that might be implemented to secure the system more effectively. Our proposed language is tested with a number of unit and integration tests. This is visualized in the paper with two real cyber attacks modeled and simulated.

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.

A modern vehicle contains over a hundred Electronic Control Units (ECUs) that communicate over in-vehicle networks, and can also be connected to external networks making them vulnerable to cyber attacks. To improve the security of connected vehicles, threat modeling can be applied to proactively find potential security issues and help manufacturers to design more secure vehicles. It can also be combined with probabilistic attack simulations to provide quantitative security measurements, which has not been commonly used while shown efficient in other domains. This paper reviews research in the field, showing that not much work has been done in the combined area of connected vehicles and threat modeling with attack simulations. We have implemented and conducted attack simulations on two vehicle threat models using a tool called securiCAD. Our work serves as a proof of concept of the approach and indicates that the approach is useful. Especially if more research of vehicle-specific vulnerabilities, weaknesses, and countermeasures is done in order to provide more accurate analyses, and to include this in a more tailored vehicle metamodel.

In this paper, we conduct an empirical study with the purpose of identifying common security vulnerabilities discovered in vehicles. The vulnerability information is gathered for 60 vehicle OEMs (Original Equipment Manufacturers) and common vehicle components from the National Vulnerability Database (NVD). Each vulnerability (CVE) is analyzed with respect to its software weakness type (CWE) and severity score (CVSS). 44 unique CVEs were found in NVD and analyzed. The analysis results show that about 50% of the vulnerabilities fall into the medium severity category, and the three most common software weaknesses reported are protection mechanism failure, buffer errors, and information disclosure.