A smart grid is a modern power network designed to monitor, manage, and optimise the flow of electricity. A cyber attack on such a critical system can disrupt services and pose significant risks to public safety and national infrastructure. Cyber attackers targeting critical infrastructures like smart grids often follow predictable patterns that can be modelled and analysed. Attack graphs reveal these potential multi-stage attack patterns, highlighting attacker reachability. However, due to the sheer number of paths and the large size of the graph, their analysis becomes computationally challenging. In this paper, a modular analysis of attack graphs is proposed to address this issue by decomposing the graphs into manageable clusters or communities. By representing each community as a single node, the complex graph is transformed into a high-level abstraction that is easier to interpret. Segregating large attack graphs into clusters enables efficient, time-bound analysis and ultimately aids in defense strategy formulation. 

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.

Foreseeing, mitigating and preventing cyber-attacks is more important than ever before. Advances in the field of probabilistic threat modelling can help organisations understand their own resilience profile against cyber-attacks. Previous research has proposed MAL, a meta language for capturing the attack logic of a considered domain and running attack simulations in a model that depicts the defended IT-infrastructure. While this modality is already somewhat established for proposing general threat mitigation actions, less is known about how to model containment strategies in the event that penetration already has occurred. The problem is a fundamental gap between predominant threat models in cyber-security research and containment in the incident response lifecycle. This paper presents a solution to the problem by summarizing a methodology for reasoning about containment strategies in MAL-based threat models.

Connecting critical infrastructure assets to the network is absolutely essential for modern industries. In contrast to the apparent advantages, network connectivity exposes other infrastructure vulnerabilities that can be exploited by attackers. To protect the infrastructure, precise countermeasure identification is necessary. In this regard, the objective for the security officers is to identify the optimal set of countermeasures under a variety of budgetary restrictions. Our approach is based on the Meta Attack Language framework, which allows for convenient modelling of said infrastructures, as well as for automatic generation of attack graphs describing attacks against them. We formalize the problem of the selection of countermeasures in this context. The formalization makes it possible to deal with an arbitrary number of budgets, expressing available resources of both monetary and time-like nature, and to model numerous dependencies between countermeasures, including order dependencies, mutual exclusivity, and interdependent implementation costs. We propose a flexible and scalable algorithm for the problem. The whole methodology is validated in practice on realistic models.