Trade-offs in Data-Driven False Data Injection Attacks Against the Power Grid

• Main Authors: Lakshminarayana, Subhash, Wen, Fuxi, Yau, David K.Y.
• Format: Conference Proceeding
• Language: English
• Subjects: bad data detection; BDD-bypass probability; Covariance matrices; Data-driven FDI attack; Noise measurement; Power grids; Power measurement; Sensors; sparsity of attack vector; Transmission line matrix methods; Transmission line measurements
• ISSN: 1520-6149; 2379-190X
• DOI: 10.1109/ICASSP.2018.8461493

We address the problem of constructing false data injection (FDI) attacks that can bypass the bad data detector (BDD) of a power grid. The attacker is assumed to have access to only power flow measurement data traces (collected over a limited period of time) and no other prior knowledge about the grid. Existing related algorithms are formulated under the assumption that the attacker has access to measurements collected over a long (asymptotically infinite) time period, which may not be realistic. We show that these approaches do not perform well when the attacker has a limited number of data samples only. We design an enhanced algorithm to construct FDI attack vectors in the face of limited measurements that can nevertheles bypass the BDD with high probability. Furthermore, we characterize an important trade-off between the attack's BDD-bypass probability and its sparsity, which affects the spatial extent of the attack that must be achieved. Extensive simulations using data traces collected from the MATPOWER simulator and benchmark IEEE bus systems validate our findings.