{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T07:08:46Z","timestamp":1779260926297,"version":"3.51.4"},"reference-count":28,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T00:00:00Z","timestamp":1726790400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T00:00:00Z","timestamp":1726790400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,20]]},"DOI":"10.1109\/icmlc63072.2024.10935257","type":"proceedings-article","created":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T02:39:19Z","timestamp":1743129559000},"page":"571-578","source":"Crossref","is-referenced-by-count":1,"title":["The Bifurcation Method: White-Box Observation Perturbation Attacks on Reinforcement Learning Agents on a Cyber Physical System"],"prefix":"10.1109","author":[{"given":"KIERNAN","family":"BRODA-MILIAN","sequence":"first","affiliation":[{"name":"Royal Military College of Canada, Electrical and Computer Engineering,Kingston,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"HANANE","family":"DAGDOUGUI","sequence":"additional","affiliation":[{"name":"Polytechnique Montreal, Computer and Software Engineering,Quebec,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"RANWA AL","family":"MALLAH","sequence":"additional","affiliation":[{"name":"Royal Military College of Canada, Electrical and Computer Engineering,Kingston,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","volume-title":"Cyber threat bulletin: Cy-ber threat to operational technology","author":"Security","year":"2021"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/JSYST.2023.3305757"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.35833\/MPCE.2020.000552"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2020.110618"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3111139"},{"key":"ref6","article-title":"City learn: A tutorial on reinforcement learning control for grid-interactive efficient buildings and com-munities","volume-title":"ICLR 2023 Workshop on Tackling Climate Change with Machine Learning","author":"Nweye","year":"2023"},{"key":"ref7","first-page":"1335","article-title":"A re-view of cyber-attack methods in cyber-physical power system","volume-title":"2019 IEEE 8th International Conference on Advanced Power System Automation and Protection (APAP)","author":"Li"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2016.2622686"},{"key":"ref9","first-page":"15","volume-title":"Black-loT: loT botnet of high wattage devices can dis-rupt the power grid","author":"Soltan","year":"2018"},{"issue":"arXiv:2301.04299","key":"ref10","author":"Standen","year":"2023","journal-title":"Sok: Adver-sarial machine learning attacks and defences in multi-agent reinforcement learning"},{"issue":"arXiv: 1701.04143","key":"ref11","author":"Behzadan","year":"2017","journal-title":"Vulnerability of deep re-inforcement learning to policy induction attacks"},{"issue":"arXiv: 1705.06452","key":"ref12","author":"Kos","year":"2017","journal-title":"Delving into adversarial at-tacks on deep policies"},{"issue":"arXiv: 1703.06748","key":"ref13","author":"Lin","year":"2019","journal-title":"Tactics of adversarial attack on deep reinforcement learning agents"},{"issue":"arXiv:1905.12282","key":"ref14","author":"Hussenot","year":"2020","journal-title":"Copycat: Taking control of neural policies with constant attacks"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/tdsc.2022.3143566"},{"issue":"arXiv:2110.04983","key":"ref16","author":"Chen","year":"2021","journal-title":"Un-derstanding the safety requirements for learning-based power systems operations"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2022.119688"},{"issue":"arXiv:2012.10504","key":"ref18","author":"Vazquez-Canteli","year":"2020","journal-title":"Citylearn: Standardizing research in multi-agent reinforcement learning for demand response and urban energy management"},{"key":"ref19","article-title":"Grid integration of zero net energy communi-ties","author":"Morell","year":"2017","journal-title":"California Public Utilities Commission"},{"issue":"arXiv:2206.09628","key":"ref20","author":"Yamamura","year":"2022","journal-title":"Diversified ad-versarial attacks based on conjugate gradient method"},{"key":"ref21","article-title":"Accurate, reliable and fast robustness evaluation","volume":"32","author":"Brendel","year":"2019","journal-title":"Advances in Neu-ral Information Processing Systems"},{"issue":"01069","key":"ref22","article-title":"Adversarial robust-ness toolbox v1.2.0","volume":"1807","author":"Nicolae","year":"2018","journal-title":"CoRR"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.12794\/metadc1505267"},{"key":"ref24","volume-title":"Towards deep learning models resistant to adversarial attacks","author":"Madry","year":"2018"},{"issue":"arXiv:1801.01290","key":"ref25","author":"Haarnoja","year":"2018","journal-title":"Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor"},{"key":"ref26","volume-title":"Adversarial robust-ness - theory and practice","author":"Madry"},{"key":"ref27","volume-title":"Torchdrift","author":"Viehmann","year":"2024"},{"issue":"arXiv: 1702.06280","key":"ref28","author":"Grosse","year":"2017","journal-title":"On the (statistical) detection of adversarial examples"}],"event":{"name":"2024 International Conference on Machine Learning and Cybernetics (ICMLC)","location":"Miyazaki, Japan","start":{"date-parts":[[2024,9,20]]},"end":{"date-parts":[[2024,9,23]]}},"container-title":["2024 International Conference on Machine Learning and Cybernetics (ICMLC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10934982\/10934998\/10935257.pdf?arnumber=10935257","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T16:35:20Z","timestamp":1743179720000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10935257\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,20]]},"references-count":28,"URL":"https:\/\/doi.org\/10.1109\/icmlc63072.2024.10935257","relation":{},"subject":[],"published":{"date-parts":[[2024,9,20]]}}}