Biblio

Cyber resiliency of Energy Delivery Systems (EDS) is critical for secure and resilient cyber infrastructure. Defense-in-depth architecture forces attackers to conduct lateral propagation until the target is compromised. Researchers developed techniques based on graph spectral matrices to model lateral propagation. However, these techniques ignore host criticality which is critical in EDS. In this paper, we model attacker's opportunity by developing three criticality metrics for each host along the path to the target. The first metric refers the opportunity of attackers before they penetrate the infrastructure. The second metric measure the opportunity a host provides by allowing attackers to propagate through the network. Along with vulnerability we also take into account the attributes of hosts and links within each path. Then, we derive third criticality metric to reflect the information flow dependency from each host to target. Finally, we provide system design for instantiating the proposed metrics for real network scenarios in EDS. We present simulation results which illustrates the effectiveness of the metrics for efficient defense deployment in EDS cyber infrastructure.

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.