Exploring the Viability of Trojan Attacks on Nuclear Machine Learning Models

Kallie McLaren; Pedro Mena; Eric Hill; Emily Elzinga; Chris Spirito; Leslie Kerby

doi:10.13182/T128-42022

Exploring the Viability of Trojan Attacks on Nuclear Machine Learning Models

Kallie McLaren
, Pedro Mena
, Eric Hill
, Emily Elzinga
, Chris Spirito
, Leslie Kerby

Research output: Contribution to journal › Conference article › peer-review

Original language	English
Pages (from-to)	613-616
Number of pages	4
Journal	Transactions of the American Nuclear Society
Volume	128
Early online date	Jun 14 2023
DOIs	https://doi.org/10.13182/T128-42022
State	Published - 2023
Event	2023 Transactions of the American Nuclear Society Annual Meeting and Technology Expo, ANS 2023 - Indianapolis, United States Duration: Jun 11 2023 → Jun 14 2023

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10.13182/T128-42022

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@article{e666c35787fe475eb4a34b838c2b5d50,

title = "Exploring the Viability of Trojan Attacks on Nuclear Machine Learning Models",

author = "Kallie McLaren and Pedro Mena and Eric Hill and Emily Elzinga and Chris Spirito and Leslie Kerby",

note = "Funding Information: Special thanks to Dr. Fan Zhang, Patience Lamb and the iFAN lab team at Georgia Tech for providing data. Funding for this work was provided by the U.S Department of Energy.; 2023 Transactions of the American Nuclear Society Annual Meeting and Technology Expo, ANS 2023 ; Conference date: 11-06-2023 Through 14-06-2023",

year = "2023",

doi = "10.13182/T128-42022",

language = "English",

volume = "128",

pages = "613--616",

journal = "Transactions of the American Nuclear Society",

issn = "0003-018X",

publisher = "American Nuclear Society",

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TY - JOUR

T1 - Exploring the Viability of Trojan Attacks on Nuclear Machine Learning Models

AU - McLaren, Kallie

AU - Mena, Pedro

AU - Hill, Eric

AU - Elzinga, Emily

AU - Spirito, Chris

AU - Kerby, Leslie

N1 - Funding Information: Special thanks to Dr. Fan Zhang, Patience Lamb and the iFAN lab team at Georgia Tech for providing data. Funding for this work was provided by the U.S Department of Energy.

PY - 2023

Y1 - 2023

UR - https://www.scopus.com/pages/publications/85180623042

U2 - 10.13182/T128-42022

DO - 10.13182/T128-42022

M3 - Conference article

AN - SCOPUS:85180623042

SN - 0003-018X

VL - 128

SP - 613

EP - 616

JO - Transactions of the American Nuclear Society

JF - Transactions of the American Nuclear Society

T2 - 2023 Transactions of the American Nuclear Society Annual Meeting and Technology Expo, ANS 2023

Y2 - 11 June 2023 through 14 June 2023

ER -

Exploring the Viability of Trojan Attacks on Nuclear Machine Learning Models

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