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\begin{document}

\title{Detecting Malicious Data in Distribution System State Estimation using
Feature Attribution}



\author[1]{Afia Afrin}%
\author[2]{Omid Ardakanian}%
\affil[1]{University of Alberta}%
\affil[2]{Affiliation not available}%


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  \date{June 11, 2024}


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\begin{abstract}
The digital transformation of power distribution system has increased
the demand for Distribution System State Estimation (DSSE) techniques
that are robust to adversarial perturbations in addition to noise. We
design a versatile method for detecting stealthy data manipulation
attacks on DSSE, drawing on a model-agnostic attribution method that
quantifies the contribution of each input feature to the state
estimation result. The key intuition for this work is that data
manipulation attacks, including adversarial and false data injection
attacks, generally have a discernible effect on this feature saliency
measure. Through extensive numerical simulation, we corroborate that the
proposed method reliably detects various data manipulation attacks,
outperforming the most prominent detection methods from the previous
work.%
\end{abstract}%



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