Vulnerability Assessment of AC State Estimation With Respect to False Data Injection Cyber-Attacks

By Gabriela Hug, Joseph Andrew Giampapa. IEEE Transactions on Smart Grid, 2012

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A novel method to detect bad data injection attack in smart grid (2013, background): “Hug and Giampapa [3] assessed the vulnerability of AC state estimation with respect to false data injection cyber-attacks.”
Detecting False Data Injection Attacks in AC State Estimation (2015, background): “Reference [8] expands the dc false data injection into ac false data injection.”
Online False Data Injection Attack Detection With Wavelet Transform and Deep Neural Networks (2018, methodology): “The detailed attack vector construction process is elaborated in [13] and [34].”
Cyber-Physical Power System (CPPS): A Review on Modeling, Simulation, and Analysis With Cyber Security Applications (2020, background): “In the case of the AC power ﬂow model, the process takes more time and does not converge to the optimal global solution [69], [125], [126].”
A Review of False Data Injection Attacks Against Modern Power Systems (2017, background): “Hug and Giampapa [24] proposed that with the AC model, if a2 = h2( x 1, x 2 + c) − h2( x 1, x 2), then: LNRbad = ∥∥zbad − h ( x̂bad )∥∥ = ∥∥z + a − h(x̂ + c)∥∥ = ∥∥∥∥ ( z1 z2 + a2 ) − ( h1 ( x̂1 ) h2 ( x̂1, x̂2 + c ) )∥∥∥∥ = ∥ ∥∥∥ ( z1 z2 ) − ( h1 ( x̂1 ) h2 ( x̂1, x̂2 ) )∥∥∥∥ = ∥∥z − h(x̂)∥∥ = LNR ”