Detecting and Mitigating Data Integrity Attacks on Distributed Algorithms for Optimal Power Flow using Machine Learning

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dc.contributor.authorHarris, Rachel
dc.contributor.authorMolzahn, Daniel
dc.date.accessioned2023-12-26T18:39:52Z
dc.date.available2023-12-26T18:39:52Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2023.382
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.other2e2f2001-f803-48fa-94b5-1df855a5adeb
dc.identifier.urihttps://hdl.handle.net/10125/106765
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectResilient Networks
dc.subjectcybersecurity
dc.subjectdata integrity attack
dc.subjectdistributed optimization
dc.subjectoptimal power flow
dc.titleDetecting and Mitigating Data Integrity Attacks on Distributed Algorithms for Optimal Power Flow using Machine Learning
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractUsing distributed algorithms, multiple computing agents can coordinate their operations by jointly solving optimal power flow problems. However, cyberattacks on the data communicated among agents may maliciously alter the behavior of a distributed algorithm. To improve cybersecurity, this paper proposes a machine learning method for detecting and mitigating data integrity attacks on distributed algorithms for solving optimal power flow problems. In an offline stage with trustworthy data, agents train and share machine learning models of their local subproblems. During online execution, each agent uses the trained models from neighboring agents to detect cyberattacks using a reputation system and then mitigate their impacts. Numerical results show that this method reliably, accurately, and quickly detects data integrity attacks and effectively mitigates their impacts to achieve near-feasible and near-optimal operating points.
dcterms.extent10 pages
prism.startingpage3170

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