TY - GEN
T1 - Detection of false data injection attack using graph signal processing for the power grid
AU - Ramakrishna, Raksha
AU - Scaglione, Anna
PY - 2019/11
Y1 - 2019/11
N2 - In this paper we revisit the problem of False Data Injection (FDI) attacks to electric power systems synchrophasors measurements through the lens of graph signal processing (GSP). First, we introduce a physics based model that supports the empirical evidence that Phasor Measurement Unit (PMU) data are low-pass graph signals. This insight is used to investigate how one can leverage such a structure to construct more effective bad data detection (BDD) algorithms that can detect FDI attack signatures through appropriate utilization of the features of the PMU graph-signal.
AB - In this paper we revisit the problem of False Data Injection (FDI) attacks to electric power systems synchrophasors measurements through the lens of graph signal processing (GSP). First, we introduce a physics based model that supports the empirical evidence that Phasor Measurement Unit (PMU) data are low-pass graph signals. This insight is used to investigate how one can leverage such a structure to construct more effective bad data detection (BDD) algorithms that can detect FDI attack signatures through appropriate utilization of the features of the PMU graph-signal.
UR - http://www.scopus.com/inward/record.url?scp=85079288407&partnerID=8YFLogxK
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U2 - 10.1109/GlobalSIP45357.2019.8969373
DO - 10.1109/GlobalSIP45357.2019.8969373
M3 - Conference contribution
T3 - GlobalSIP 2019 - 7th IEEE Global Conference on Signal and Information Processing, Proceedings
BT - GlobalSIP 2019 - 7th IEEE Global Conference on Signal and Information Processing, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2019
Y2 - 11 November 2019 through 14 November 2019
ER -