IMPROVING BULK POWER SYSTEM RESILIENCE BY RANKING CRITICAL NODES IN THE VULNERABILITY GRAPH

摘要

The paper focuses on the critical node identification which can be used to rank the nodes belong to the corporate and control system network of Bulk Power System (BPS). We are proposing here an MADM (Multiple Attribute Decision Making) based ranking algorithm using a multi-layered directed acyclic graph (DAG) model to rank the critical nodes in the network. Ranking of the critical nodes can contribute to the resilience improvement process. Our proposed MVNRank (Multiple Vulnerability Node Rank) algorithm takes into account the exploit and impact scores of vulnerabilities as quantified by CVSS (Common Vulnerability Scoring System) and the severity level of each vulnerability. The algorithm also takes into account the asset value, degree centrality and node's distance from the target network in the vulnerability graph. The pseudocode for the resilience improvement using the node ranking is provided and necessary simulation results are presented to justify the node ranking importance.