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Critical infrastructure and contemporary business organizations are experiencing an ongoing paradigm shift of business towards more collaboration and agility. On the one hand, this shift seeks to enhance business efficiency, coordinate large-scale distribution operations, and manage complex supply chains. But, on the other hand, it makes traditional security practices such as firewalls and other perimeter defenses insufficient. Therefore, concerns over risks like terrorism, crime, and business revenue loss increasingly impose the need for enhancing and managing security within the boundaries of these systems so that unwanted incidents (e.g., potential intrusions) can still be detected with higher probabilities. To this end, critical infrastructure organizations step up their efforts to investigate new possibilities for actively engaging in situational awareness practices to ensure a high level of persistent monitoring as well as on-site observation.
Compliance with security standards is necessary to ensure that organizations meet regulatory requirements mostly shaped by a set of best practices. Nevertheless, it does not necessarily result in a coherent security strategy that considers the different aims and practical constraints of each organization. In this regard, there is an increasingly growing demand for risk-based security management approaches that enable critical infrastructures to focus their efforts on mitigating the risks to which they are exposed. Broadly speaking, security management involves the identification, assessment, and evaluation of long-term (or overall) objectives and interests as well as the means of achieving them.
Due to the critical role of such systems, their decision-makers tend to enhance the system resilience against very unpleasant outcomes and severe consequences. That is, they seek to avoid decision options associated with likely extreme risks in the first place. Practically speaking, this risk attitude can significantly influence the decision-making process in such critical organizations. Towards incorporating the aversion to extreme risks into security management decisions, this thesis investigates thoroughly the capabilities of a recently emerged theory of games with payoffs that are probability distributions. Unlike traditional optimization techniques, this theory provides an alternative decision technique that is more robust to extreme risks and uncertainty. Furthermore, this thesis proposes a new method that gives a decision maker more control over the decision-making process through defining loss regions with different importance levels according to people's risk attitudes. In this way, the static decision analysis used in the distribution-valued games is transformed into a dynamic process to adapt to different subjective risk attitudes or account for future changes in the decision caused by a learning process or other changes in the context.
Throughout their different parts, this thesis shows how theoretical models, simulation, and risk assessment models can be combined into practical solutions. In this context, it deals with three facets of security management: allocating limited security resources, prioritizing security actions, and tweaking decision making. Finally, the author discusses experiences and limitations distilled from this research and from investigating the new theory of games, which can be taken into account in future approaches.
In smart grids, managing and controlling power operations are supported by information
and communication technology (ICT) and supervisory control and data acquisition (SCADA) systems. The increasing adoption of new ICT assets in smart grids is making smart grids vulnerable to cyber threats, as well as raising numerous concerns about the adequacy of current security approaches.
As a single act of penetration is often not sufficient for an attacker to achieve his/her goal, multistage cyber attacks may occur. Due to the interdependence between the power grid and the communication network, a multistage cyber attack not only affects the cyber system but impacts the physical system. This thesis investigates an application-oriented stochastic game-theoretic cyber threat assessment framework, which is strongly related to the information security risk management process as standardized in ISO/IEC 27005. The proposed cyber threat assessment framework seeks to address the specific challenges (e.g., dynamic changing attack scenarios and understanding cascading effects) when performing threat assessments for multistage cyber attacks in smart grid communication networks.
The thesis looks at the stochastic and dynamic nature of multistage cyber attacks in smart grid use cases and develops a stochastic game-theoretic model to capture the interactions of the attacker and the defender in multistage attack scenarios. To provide a flexible and practical payoff formulation for the designed stochastic game-theoretic model, this thesis presents a mathematical analysis of cascading failure propagation (including both interdependency cascading failure propagation and node overloading cascading failure propagation) in smart grids. In addition, the thesis quantifies the characterizations of disruptive effects of cyber attacks on physical power grids.
Furthermore, this thesis discusses, in detail, the ingredients of the developed stochastic game-theoretic model and presents the implementation steps of the investigated stochastic game-theoretic cyber threat assessment framework. An application of the proposed cyber threat assessment framework for evaluating a demonstrated multistage cyber attack scenario in smart grids is shown. The cyber threat assessment framework can be integrated into an existing risk management process, such as ISO 27000, or applied as a standalone threat assessment process in smart grid use cases.