| Title | Vulnerability analysis and consequences of false data injection attack on power system state estimation |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Liang, J., Sankar, L., Kosut, O. |
| Conference Name | 2017 IEEE Power Energy Society General Meeting |
| Date Published | jul |
| Keywords | AC state estimation, bi-level optimization problem, compositionality, DC optimal power flow, false data injection attack, FDI attack, Human Behavior, Limiting, Load flow, Load modeling, Metrics, nonlinear system model, Optimization, physical line, power system security, power system state estimation, power transmission lines, pubcrawl, Resiliency, state estimation, vulnerability analysis, vulnerability detection |
| Abstract | An unobservable false data injection (FDI) attack on AC state estimation (SE) is introduced and its consequences on the physical system are studied. With a focus on understanding the physical consequences of FDI attacks, a bi-level optimization problem is introduced whose objective is to maximize the physical line flows subsequent to an FDI attack on DC SE. The maximization is subject to constraints on both attacker resources (size of attack) and attack detection (limiting load shifts) as well as those required by DC optimal power flow (OPF) following SE. The resulting attacks are tested on a more realistic non-linear system model using AC state estimation and ACOPF, and it is shown that, with an appropriately chosen sub-network, the attacker can overload transmission lines with moderate shifts of load. |
| DOI | 10.1109/PESGM.2017.8273736 |
| Citation Key | liang_vulnerability_2017 |
Vulnerability analysis and consequences of false data injection attack on power system state estimation