@inproceedings{ashebo_covert_2025,
author={Betelihem Kebede Ashebo and Samuel Talkington and Saman Zonouz and Daniel K. Molzahn},
title={{Covert Distribution Load Tripping Attacks}},
year = {2025},
url={https://molzahn.github.io/pubs/ashebo_talkington_zonouz_molzahn-dlc_attacks.pdf},
doi = {10.1145/3575813.3597357},
booktitle = {57th North American Power Symposium},
numpages = {5},
location = {Hartford, CT, USA},
series = {NAPS 2025}
}The increasing integration of distributed energy resources (DERs), particularly solar photovoltaic (PV) systems, has introduced new cybersecurity challenges in distribution networks. This paper presents a data-driven attack model that examines how an adversary can exploit direct load control (DLC) mechanisms to selectively disconnect downstream loads during periods of high solar generation. Such targeted load tripping forces excess PV output to flow back toward the substation transformer, potentially causing power imbalances and transformer overloading. We model both PV output and load demand as multivariate Gaussian distributions to capture their inherent temporal and spatial uncertainties. A probabilistic power imbalance metric is defined to quantify the extent of reverse flow under compromised conditions. To identify the most impactful combinations of load disconnections and timing, we employ a multi-armed bandit approach based on the Upper Confidence Bound (UCB) algorithm. Simulation results demonstrate the feasibility and effectiveness of the attack strategy under realistic variability in solar output and demand.
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