The losses arising from a system being hit by cyber attacks can be staggeringly high, but defending against such attacks can also be costly. This work proposes an attack countermeasure selection approach based on cost impact analysis that takes into account the impacts of actions by both the attacker and the defender. We consider a networked system providing services whose provision depends on other components in the network. We model the costs and losses to service availability from compromises and defensive actions to the components, and show that while containment of the attack can be an effective defensive strategy, it can be more cost-efficient to allow parts of the attack to continue further whilst focusing on recovering services to a functional state. Based on this insight, we build a countermeasure selection method that chooses the most cost-effective action based on its impact on expected losses and costs over a given time horizon. Our method is evaluated using simulations in synthetic graphs representing network dependencies and vulnerabilities, and found to perform well in comparison to alternatives.
Our work differs from most of the existing literature on resilience by focusing on the actions and investment choices during an ongoing event (attack) and the recovery phase, instead of preparatory planning and capability investment. This focus is intended to address the evolving nature of systems, and adaptation to conditions such as loss of confidentiality or network unavailability etc. Most cyber resilience works have focused on the planning and design stage, such as @cite_14 @cite_3 @cite_15 @cite_10 . Additionally, papers considering reactive response and recovery apply to narrow settings which do not apply to our work. For example, the approaches by @cite_18 @cite_2 only apply to settings where a control action to correct for a deviation from desired performance is easy to determine in advance, and to apply automatically.
@cite_18 @cite_14 @cite_3 @cite_2 @cite_15 @cite_10
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Building resilience into today's complex infrastructures is critical to the daily functioning of society and its ability to withstand and recover from natural disasters, epidemics, and cyber-threats. This study proposes quantitative measures that capture and implement the definition of engineering resilience advanced by the National Academy of Sciences. The approach is applicable across physical, information, and social domains. It evaluates the critical functionality, defined as a performance function of time set by the stakeholders. Critical functionality is a source of valuable information, such as the integrated system resilience over a time interval, and its robustness. The paper demonstrates the formulation on two classes of models: 1) multi-level directed acyclic graphs, and 2) interdependent coupled networks. For both models synthetic case studies are used to explore trends. For the first class, the approach is also applied to the Linux operating system. RESULTS indicate that desired resilience and robustness levels are achievable by trading off different design parameters, such as redundancy, node recovery time, and backup supply available. The nonlinear relationship between network parameters and resilience levels confirms the utility of the proposed approach, which is of benefit to analysts and designers of complex systems and networks. Language: en The complex interconnections between various critical infrastructure sectors make the system of systems (SoS) vulnerable to failures and highlight the importance of robustness and resilience. To this end, we first establish holistic probabilistic networks to model the interdependencies between infrastructure components. To capture the underlying failure and recovery dynamics, we further propose a Markov decision processes (MDP) model in which the response policy determines a long-term performance. To address the challenge of a large dimensionality, we exploit the sparsity of the network interconnections and solve an approximate linear program by the variable elimination, which leads to a distributed control policy under mild assumptions. Finally, we use a case study of the interdependent power and subway systems to corroborate the results and show that the optimal resilience resource planning and allocation can reduce the failure probability and mitigate the impact of failures caused by natural or artificial disasters. Successful recovery from a disrupted state to maintain optimal performance is a key feature that a resilient complex engineered system should have. In the engineering design community, the current focus of engineering resilience research is primarily directed toward improving overall system performance in the presence of likelihood failures. Little attention has been given to the study of how the system responds during and or after the occurrence of a failure event. This paper proposes the use of control theory as a strategy to enable resilient behavior in complex engineered systems. Control theory has various benefits in its application to a resilient engineered system, with the main advantage being its ability to regulate and govern system states, even while the failure is taking place. In the context of implementation within a complex engineered system, such a controller should be designed such that, when a disturbance occurs, the controller should simultaneously be able to take timely action to correct the shift in system performance. To date, the fusion of control theory with engineering resilience has not been explored in-depth by the engineering design community. This paper, thus, presents a resilience modeling and analysis approach using fundamental control theory. The resilience of a power distribution system is employed as a case study to demonstrate the effectiveness of the proposed approach. The presented study also expects to aid in the concurrent development of resilience functions in complex engineered systems under uncertainty. Abstract This paper proposes a new multi-stage framework to analyze infrastructure resilience. For each stage, a series of resilience-based improvement strategies are highlighted and appropriate correlates of resilience identified, to then be combined for establishing an expected annual resilience metric adequate for both single hazards and concurrent multiple hazard types. Taking the power transmission grid in Harris County, Texas, USA, as a case study, this paper compares an original power grid model with several hypothetical resilience-improved models to quantify their effectiveness at different stages of their response evolution to random hazards and hurricane hazards. Results show that the expected annual resilience is mainly compromised by random hazards due to their higher frequency of occurrence relative to hurricane hazards. In addition, under limited resources, recovery sequences play a crucial role in resilience improvement, while under sufficient availability of resources, deploying redundancy, hardening critical components and ensuring rapid recovery are all effective responses regardless of their ordering. The expected annual resilience of the power grid with all three stage improvements increases 0.034 compared to the original grid. Although the improvement is small in absolute magnitude due to the high reliability of real power grids, it can still save millions of dollars per year as assessed by energy experts. This framework can provide insights to design, maintain, and retrofit resilient infrastructure systems in practice.