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.
This paper presents an unifying graph-based model for representing the infrastructure, behavior and missions of an enterprise. We describe how the model can be used to achieve resiliency against a wide class of failures and attacks. We introduce an algorithm for recommending resilience establishing actions based on dynamic updates to the models. Without loss of generality, we show the effectiveness of the algorithm for preserving latency based quality of service (QoS). Our models and the recommendation algorithms are implemented in a software framework that we seek to release as an open source framework for simulating resilient cyber systems. The Internet has become essential to all aspects of modern life, and thus the consequences of network disruption have become increasingly severe. It is widely recognised that the Internet is not sufficiently resilient, survivable, and dependable, and that significant research, development, and engineering is necessary to improve the situation. This paper provides an architectural framework for resilience and survivability in communication networks and provides a survey of the disciplines that resilience encompasses, along with significant past failures of the network infrastructure. A resilience strategy is presented to defend against, detect, and remediate challenges, a set of principles for designing resilient networks is presented, and techniques are described to analyse network resilience. Supply-demand processes take place on a large variety of real-world networked systems ranging from power grids and the internet to social networking and urban systems. In a modern infrastructure, supply-demand systems are constantly expanding, leading to constant increase in load requirement for resources and consequently, to problems such as low efficiency, resource scarcity, and partial system failures. Under certain conditions global catastrophe on the scale of the whole system can occur through the dynamical process of cascading failures. We investigate optimization and resilience of time-varying supply-demand systems by constructing network models of such systems, where resources are transported from the supplier sites to users through various links. Here by optimization we mean minimization of the maximum load on links, and system resilience can be characterized using the cascading failure size of users who fail to connect with suppliers. We consider two representative classes of supply schemes: load driven supply and fix fraction supply. Our findings are: (1) optimized systems are more robust since relatively smaller cascading failures occur when triggered by external perturbation to the links; (2) a large fraction of links can be free of load if resources are directed to transport through the shortest paths; (3) redundant links in the performance of the system can help to reroute the traffic but may undesirably transmit and enlarge the failure size of the system; (4) the patterns of cascading failures depend strongly upon the capacity of links; (5) the specific location of the trigger determines the specific route of cascading failure, but has little effect on the final cascading size; (6) system expansion typically reduces the efficiency; and (7) when the locations of the suppliers are optimized over a long expanding period, fewer suppliers are required. These results hold for heterogeneous networks in general, providing insights into designing optimal and resilient complex supply-demand systems that expand constantly in time. 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. 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 There are many models and metrics developed to study the resilience of networks. Eigenvalues are the roots of the characteristic polynomial for a given graph and are mathematically rigorous compared to a statistical measure such as degree distribution. The graph energy is the sum of absolute values of eigenvalues; there is a subtle difference between the adjacency, Laplacian, and normalized Laplacian graph energy calculations. Our primary objective in this paper is to understand what different graph energy mean from a network resilience point of view. We calculate the adjacency, Laplacian, and normalized Laplacian graph energies on four backbone networks under targeted node and link attack scenarios. While adjacency and Laplacian graph energy decrease with node and link attacks, the normalized Laplacian energy increases with link attacks converging to a maximum value equal to the network order. The structural similarities of physical-level topologies is revealed by the close values of adjacency and Laplacian energies.