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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.

Abstract Threat modeling is of increasing importance to IT security, and it is a complex and resource demanding task. The aim of automating threat modeling is to simplify model creation by using data that are already available. However, the collected data often lack context; this can make the automated models less precise in terms of domain knowledge than those created by an expert human modeler. The lack of domain knowledge in modeling automation can be addressed with ontologies. In this paper, we introduce an ontology framework to improve automatic threat modeling. The framework is developed with conceptual modeling and validated using three different datasets: a small scale utility lab, water utility control network, and university IT environment. The framework produced successful results such as standardizing input sources, removing duplicate name entries, and grouping application software more logically.

Modeling IT architecture is a complex, time consuming, and error prone task. However, many systems produce information that can be used for automating modeling. Early studies show that this is a feasible approach if we can overcome certain obstacles. Often more than one source is needed in order to cover the data requirements of an IT architecture model; and the use of multiple sources means that heterogeneous data needs to be merged. Moreover, the same collection of data might be useful for creating more than one kind of models for decision support. IT architecture is constantly changing and data sources provide information that can deviate from reality to some degree. There can be problems with varying accuracy (e.g. actuality and coverage), representation (e.g. data syntax and file format), or inconsistent semantics. Thus, integration of heterogeneous data from different sources needs to handle data quality problems of the sources. This can be done by using probabilistic models. In the field of truth discovery, these models have been developed to track data source trustworthiness in order to help solving conflicts while making quality issues manageable for automatic modeling. We build upon previous research in modeling automation and propose a framework for merging data from multiple sources with a truth discovery algorithm to create multiple IT architecture models. The usefulness of the proposed framework is demonstrated in a study where models using three tools are created, namely; Archi, securiCAD, and EMFTA.

Recent contributions to information systems theory suggest that the primary role of a firm’s information technology (IT) architecture is to facilitate, and therefore ensure the continued alignment of a firm’s IT investments with a constantly changing business environment. Despite the advances we lack robust methods with which to operationalize enterprise IT architecture, in a way that allows us to analyze performance, in terms of the ability to adapt and evolve over time. We develop a methodology for analyzing enterprise IT architecture based on “Design Structure Matrices” (DSMs), which capture the coupling between all components in the architecture. Our method addresses the limitations of prior work, in that it i) captures the architecture “in-use” as opposed to high level plans or conceptual models; ii) identifies discrete layers in the architecture associated with different technologies; iii) reveals the “flow of control” within the architecture; and iv) generates measures that can be used to analyze performance. We apply our methodology to a dataset from a large pharmaceutical firm. We show that measures of coupling derived from an IT architecture DSM predict IT modifiability – defined as the cost to change software applications. Specifically, applications that are tightly coupled cost significantly more to change.

The Internet of Things (IoT) has disrupted the IT landscape drastically, and Long Range Wide Area Network (LoRaWAN) is one specification that enables these IoT devices to have access to the Internet. Former security analyses have suggested that the gateways in LoRaWAN in their current state are susceptible to a wide variety of malicious attacks, which can be notoriously difficult to mitigate since gateways are seen as obedient relays by design. These attacks, if not addressed, can cause malfunctions and loss of efficiency in the network traffic. As a solution to this unique problem, this paper presents a novel certificate authentication technique that enhances the cyber security of gateways in the LoRaWAN network. The proposed technique considers a public key infrastructure (PKI) solution that considers a two-tier certificate authority (CA) setup, such as a root-CA and intermediate-CA. This solution is promising, as the simulation results validate that about 66.67% of the packets that are arriving from an illegitimate gateway (GW) are discarded in our implemented secure and reliable solution.

Abstract Background In the coming years, the increase of automation in electricity distribution grids, controlled by ICT, will bring major consequences to the cyber security posture of the grids. Automation plays an especially important role in load balancing of renewable energy where distributed generation is balanced to load in a way that the grid stability is ensured. Threats to the load balancing and the smart grid in general arise from the activities of misbehaving or rouge actors in combination with poor design, implementation, or configuration of the system that makes it vulnerable. It is urgent to conduct an in-depth analysis about the feasibility and imminency of these potential threats ahead of a cyber catastrophy. This paper presents a cyber security evaluation of the ICT part of the smart grid with a focus on load balancing of renewable energy. Method The work builds on a load balancing centered smart grid reference architecture model that is designed as part of the evaluation with the help of SCADA system and smart grid experts. The smart grid load balancing architecture represented by the model is then analyzed using a threat modelling approach that is encapsulated in a tool called securiCAD. Countermeasures are introduced in the model to measure how much each improve the cyber security of the smart grid. Results The analysis shows that the main threat comes from the internet and is directly dependant on the level of internet access office users have coupled with their level of access on the OT zone. Supply chain attacks are also of great concern, i.e. the compromising of the software/hardware vendor with the objective of feeding rogue updates to assets, typically to install a backdoor. The general takeaway defense-wise is that it is of the utmost importance to increase efforts in securing the smart Grid in all the ways possible as they appear to be generally complementary. Discussion The obtained results raise concerns whether the architecture of the smart grid still remains satisfactory in today’s state of the cyberspace and the increased presence and sophistication of cyber threats. There are also concerns whether the proposed security measures, regardless of their evaluated effectiveness, are realistically implementable from both financial and practical point of view. Conclusions There is no silver bullet available to achieve full protection against cyber attacks. The smart grid remains a network of IT/OT machines with dataflows going between them. State-sponsored hackers given enough time and regardless of the defences in place will eventually make their way into a critical infrastructure such as the smart grid. While mitigations will not eliminate the threats, they will increase the cyber resilience of the infrastructure by increasing both its time frame and effectiveness. As such, in a time where usability, efficiency and practicality are at the front of every domain, innovations regarding these aspects should really be carried out with strong security in mind.

The Multi-Attribute Prediction Language (MAPL), an analysis metamodel for non-functional qualities of system architectures, is introduced. MAPL features automate analysis in five non-functional areas: service cost, service availability, data accuracy, application coupling, and application size. In addition, MAPL explicitly includes utility modeling to make trade-offs between the qualities. The article introduces how each of the five non-functional qualities are modeled and quantitatively analyzed based on the ArchiMate standard for enterprise architecture modeling and the previously published Predictive, Probabilistic Architecture Modeling Framework, building on the well-known UML and OCL formalisms. The main contribution of MAPL lies in the probabilistic use of multi-attribute utility theory for the trade-off analysis of the non-functional properties. Additionally, MAPL proposes novel model-based analyses of several non-functional attributes. We also report how MAPL has iteratively been developed using multiple case studies.

Authorization and its enforcement, access control, have stood at the beginning of the art and science of information security, and remain being crucial pillar of security in the information technology (IT) and enterprises operations. Dozens of different models of access control have been proposed. Although Enterprise Architecture as the discipline strives to support the management of IT, support for modeling access policies in enterprises is often lacking, both in terms of supporting the variety of individual models of access control nowadays used, and in terms of providing a unified ontology capable of flexibly expressing access policies for all or the most of the models. This study summarizes a number of existing models of access control, proposes a unified metamodel mapped to ArchiMate, and illustrates its use on a selection of example scenarios and two business cases.