| Title | Algorithmic Approaches to Characterizing Power Flow Cyber-Attack Vulnerabilities |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Tuttle, Michael, Wicker, Braden, Poshtan, Majid, Callenes, Joseph |
| Conference Name | 2019 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT) |
| Keywords | algorithmic approaches, attack models, composability, contingency analysis, control systems, convergence, cyber-attacks, Economic dispatch, Indexes, Load flow, Metrics, Numerical Resilience, power engineering computing, power flow constraints, power flow cyber-attack vulnerabilities, power grid control systems, Power Grid Vulnerability Assessment, power grids, power related errors, power system security, power system stability, pubcrawl, resilience, Resiliency, SCADA, security of data, Smart grid, state estimation, Voltage measurement |
| Abstract | As power grid control systems become increasingly automated and distributed, security has become a significant design concern. Systems increasingly expose new avenues, at a variety of levels, for attackers to exploit and enable widespread disruptions and/or surveillance. Much prior work has explored the implications of attack models focused on false data injection at the front-end of the control system (i.e. during state estimation) [1]. Instead, in this paper we focus on characterizing the inherent cyber-attack vulnerabilities with power flow. Power flow (and power flow constraints) are at the core of many applications critical to operation of power grids (e.g. state estimation, economic dispatch, contingency analysis, etc.). We propose two algorithmic approaches for characterizing the vulnerability of buses within power grids to cyber-attacks. Specifically, we focus on measuring the instability of power flow to attacks which manifest as either voltage or power related errors. Our results show that attacks manifesting as voltage errors are an order of magnitude more likely to cause instability than attacks manifesting as power related errors (and 5x more likely for state estimation as compared to power flow). |
| DOI | 10.1109/ISGT.2019.8791672 |
| Citation Key | tuttle_algorithmic_2019 |
Algorithmic Approaches to Characterizing Power Flow Cyber-Attack Vulnerabilities