Biblio

In this work, we use a subjective approach to compute cyber resilience metrics for industrial control systems. We utilize the extended form of the R4 resilience framework and span the metrics over physical, technical, and organizational domains of resilience. We develop a qualitative cyber resilience assessment tool using the framework and a subjective questionnaire method. We make sure the questionnaires are realistic, balanced, and pertinent to ICS by involving subject matter experts into the process and following security guidelines and standards practices. We provide detail mathematical explanation of the resilience computation procedure. We discuss several usages of the qualitative tool by generating simulation results. We provide a system architecture of the simulation engine and the validation of the tool. We think the qualitative simulation tool would give useful insights for industrial control systems' overall resilience assessment and security analysis.

In this paper, we analyze the cyber resilience for the energy delivery systems (EDS) using critical system functionality (CSF). Some research works focus on identification of critical cyber components and services to address the resiliency for the EDS. Analysis based on the devices and services excluding the system behavior during an adverse event would provide partial analysis of cyber resilience. To address the gap, in this work, we utilize the vulnerability graph representation of EDS to compute the system functionality under adverse condition. We use network criticality metric to determine CSF. We estimate the criticality metric using graph Laplacian matrix and network performance after removing links (i.e., disabling control functions, or services). We model the resilience of the EDS using CSF, and system recovery curve. We also provide a comprehensive analysis of cyber resilience by determining the critical devices using TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and AHP (Analytical Hierarchy Process) methods. We present use cases of EDS illustrating the way control functions and services in EDS map to the vulnerability graph model. The simulation results show that we can estimate the resilience metric using different types of graphs that may assist in making an informed decision about EDS resilience.