{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T01:53:35Z","timestamp":1769738015357,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":92,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T00:00:00Z","timestamp":1670198400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["1565523,DGE-2039655"],"award-info":[{"award-number":["1565523,DGE-2039655"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"DOE","award":["DE-AC52-07NA27344"],"award-info":[{"award-number":["DE-AC52-07NA27344"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,12,5]]},"DOI":"10.1145\/3564625.3567969","type":"proceedings-article","created":{"date-parts":[[2022,12,3]],"date-time":"2022-12-03T01:01:29Z","timestamp":1670029289000},"page":"13-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["DRAGON: Deep Reinforcement Learning for Autonomous Grid Operation and Attack Detection"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3095-1619","authenticated-orcid":false,"given":"Matthew","family":"Landen","sequence":"first","affiliation":[{"name":"Georgia Institute of Technology, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1078-8092","authenticated-orcid":false,"given":"Keywhan","family":"Chung","sequence":"additional","affiliation":[{"name":"Lawrence Livermore National Laboratory, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4403-5745","authenticated-orcid":false,"given":"Moses","family":"Ike","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5501-2648","authenticated-orcid":false,"given":"Sarah","family":"Mackay","sequence":"additional","affiliation":[{"name":"Lawrence Livermore National Laboratory, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6026-8875","authenticated-orcid":false,"given":"Jean-Paul","family":"Watson","sequence":"additional","affiliation":[{"name":"Lawrence Livermore National Laboratory, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2761-1277","authenticated-orcid":false,"given":"Wenke","family":"Lee","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, United States of America"}]}],"member":"320","published-online":{"date-parts":[[2022,12,5]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2933020"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3243734.3243781"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2021.3102101"},{"key":"e_1_3_2_1_4_1","unstructured":"Chen Binbin. 2021. AsprinChina\/L2RPN_NIPS_2020_a_PPO_ Solution. https:\/\/github.com\/AsprinChina\/L2RPN_NIPS_2020_a_PPO_Solution original-date: 2020-11-12T03:53:04Z.  Chen Binbin. 2021. AsprinChina\/L2RPN_NIPS_2020_a_PPO_ Solution. https:\/\/github.com\/AsprinChina\/L2RPN_NIPS_2020_a_PPO_Solution original-date: 2020-11-12T03:53:04Z."},{"key":"e_1_3_2_1_5_1","volume-title":"Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=IDFQI9OY6K","author":"Boecking Benedikt","year":"2021","unstructured":"Benedikt Boecking , Willie Neiswanger , Eric Xing , and Artur Dubrawski . 2021 . Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=IDFQI9OY6K Benedikt Boecking, Willie Neiswanger, Eric Xing, and Artur Dubrawski. 2021. Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=IDFQI9OY6K"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Francesco Cadini Gian\u00a0Luca Agliardi and Enrico Zio. 2017. A Modeling and Simulation Framework for the Reliability\/Availability Assessment of a Power Transmission Grid Subject to Cascading Failures under Extreme Weather Conditions. Applied energy 185(2017) 267\u2013279.  Francesco Cadini Gian\u00a0Luca Agliardi and Enrico Zio. 2017. A Modeling and Simulation Framework for the Reliability\/Availability Assessment of a Power Transmission Grid Subject to Cascading Failures under Extreme Weather Conditions. Applied energy 185(2017) 267\u2013279.","DOI":"10.1016\/j.apenergy.2016.10.086"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2010.2099234"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3134600.3134640"},{"key":"e_1_3_2_1_9_1","volume-title":"Proceedings of the USENIX Security Symposium. 911\u2013927","author":"Cho Kyong-Tak","year":"2016","unstructured":"Kyong-Tak Cho and Kang\u00a0 G Shin . 2016 . Fingerprinting Electronic Control Units for Vehicle Intrusion Detection . In Proceedings of the USENIX Security Symposium. 911\u2013927 . Kyong-Tak Cho and Kang\u00a0G Shin. 2016. Fingerprinting Electronic Control Units for Vehicle Intrusion Detection. In Proceedings of the USENIX Security Symposium. 911\u2013927."},{"key":"e_1_3_2_1_10_1","unstructured":"Alfredo\u00a0V Clemente Humberto\u00a0N Castej\u00f3n and Arjun Chandra. 2017. Efficient Parallel Methods for Deep Reinforcement Learning. arXiv preprint arXiv:1705.04862(2017).  Alfredo\u00a0V Clemente Humberto\u00a0N Castej\u00f3n and Arjun Chandra. 2017. Efficient Parallel Methods for Deep Reinforcement Learning. arXiv preprint arXiv:1705.04862(2017)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2018.2873217"},{"key":"e_1_3_2_1_12_1","volume-title":"Falcon Complete: Managed Detection and Response. CrowdStrike Website. https:\/\/www.crowdstrike.com\/endpoint-security-products\/falcon-complete\/.","year":"2022","unstructured":"CrowdStrike. 2022 . Falcon Complete: Managed Detection and Response. CrowdStrike Website. https:\/\/www.crowdstrike.com\/endpoint-security-products\/falcon-complete\/. CrowdStrike. 2022. Falcon Complete: Managed Detection and Response. CrowdStrike Website. https:\/\/www.crowdstrike.com\/endpoint-security-products\/falcon-complete\/."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/SMARTGRID.2010.5622046"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/1824801.1824814"},{"key":"e_1_3_2_1_15_1","unstructured":"Benjamin Donnot. 2020. Grid2op- A testbed platform to model sequential decision making in power systems.. https:\/\/GitHub.com\/rte-france\/grid2op.  Benjamin Donnot. 2020. Grid2op- A testbed platform to model sequential decision making in power systems.. https:\/\/GitHub.com\/rte-france\/grid2op."},{"key":"e_1_3_2_1_16_1","volume-title":"Proceedings of the IREP Symposium.","author":"Donnot Benjamin","year":"2017","unstructured":"Benjamin Donnot , Isabelle Guyon , Marc Schoenauer , Patrick Panciatici , and Antoine Marot . 2017 . Introducing Machine Learning for Power System Operation Support . In Proceedings of the IREP Symposium. Benjamin Donnot, Isabelle Guyon, Marc Schoenauer, Patrick Panciatici, and Antoine Marot. 2017. Introducing Machine Learning for Power System Operation Support. In Proceedings of the IREP Symposium."},{"key":"e_1_3_2_1_17_1","unstructured":"Dragos Inc.2019. The Evolution of Cyber Attacks on Electric Operations. https:\/\/www.dragos.com\/blog\/industry-news\/the-evolution-of-cyber-attacks-on-electric-operations\/.  Dragos Inc.2019. The Evolution of Cyber Attacks on Electric Operations. https:\/\/www.dragos.com\/blog\/industry-news\/the-evolution-of-cyber-attacks-on-electric-operations\/."},{"key":"e_1_3_2_1_18_1","unstructured":"Electric Power Research Institute. 2022. Welcome to the AI.EPRI L2RPN challenge portal. https:\/\/www.epri.com\/l2rpn.  Electric Power Research Institute. 2022. Welcome to the AI.EPRI L2RPN challenge portal. https:\/\/www.epri.com\/l2rpn."},{"key":"e_1_3_2_1_19_1","volume-title":"Handbook of Electrical Power System Dynamics: Modeling, Stability, and Control. Vol.\u00a092","author":"Eremia Mircea","unstructured":"Mircea Eremia and Mohammad Shahidehpour . 2013. Handbook of Electrical Power System Dynamics: Modeling, Stability, and Control. Vol.\u00a092 . John Wiley & Sons . Mircea Eremia and Mohammad Shahidehpour. 2013. Handbook of Electrical Power System Dynamics: Modeling, Stability, and Control. Vol.\u00a092. John Wiley & Sons."},{"key":"e_1_3_2_1_20_1","unstructured":"FireEye. 2021. Email Security Solution. FireEye website. https:\/\/www.fireeye.com\/products\/email-security.html.  FireEye. 2021. Email Security Solution. FireEye website. https:\/\/www.fireeye.com\/products\/email-security.html."},{"key":"e_1_3_2_1_21_1","unstructured":"FireEye. 2021. Endpoint Security Software and Solutions. FireEye Website. https:\/\/www.fireeye.com\/products\/endpoint-security.html.  FireEye. 2021. Endpoint Security Software and Solutions. FireEye Website. https:\/\/www.fireeye.com\/products\/endpoint-security.html."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2019.2940890"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2016.23142"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01589354"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2017.23313"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3167132.3167305"},{"key":"e_1_3_2_1_27_1","unstructured":"J\u00a0Duncan Glover Mulukutla\u00a0S Sarma and Thomas Overbye. 2012. Power System Analysis & Design SI version. Cengage Learning.  J\u00a0Duncan Glover Mulukutla\u00a0S Sarma and Thomas Overbye. 2012. Power System Analysis & Design SI version. Cengage Learning."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijcip.2013.05.001"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.5555\/2051237.2051250"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/2664243.2664277"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/DASC-PICom-DataCom-CyberSciTec.2017.99"},{"key":"e_1_3_2_1_32_1","volume-title":"Proceedings of AAAI Fall Symposium Series.","author":"Hausknecht Matthew","year":"2015","unstructured":"Matthew Hausknecht and Peter Stone . 2015 . Deep Recurrent Q-learning for Partially Observable MDPs . In Proceedings of AAAI Fall Symposium Series. Matthew Hausknecht and Peter Stone. 2015. Deep Recurrent Q-learning for Partially Observable MDPs. In Proceedings of AAAI Fall Symposium Series."},{"key":"e_1_3_2_1_33_1","volume-title":"Proceedings of the USENIX Security Symposium. 1115\u20131132","author":"Huang Bing","year":"2019","unstructured":"Bing Huang , Alvaro\u00a0 A Cardenas , and Ross Baldick . 2019 . Not Everything is Dark and Gloomy: Power Grid Protections Against IoT Demand Attacks . In Proceedings of the USENIX Security Symposium. 1115\u20131132 . Bing Huang, Alvaro\u00a0A Cardenas, and Ross Baldick. 2019. Not Everything is Dark and Gloomy: Power Grid Protections Against IoT Demand Attacks. In Proceedings of the USENIX Security Symposium. 1115\u20131132."},{"key":"e_1_3_2_1_34_1","unstructured":"Information Trust Institution. 2013. IEEE 14-Bus System. ITI Website. https:\/\/icseg.iti.illinois.edu\/ieee-14-bus-system.  Information Trust Institution. 2013. IEEE 14-Bus System. ITI Website. https:\/\/icseg.iti.illinois.edu\/ieee-14-bus-system."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3372318.3372324"},{"key":"e_1_3_2_1_37_1","unstructured":"Max Jaderberg Valentin Dalibard Simon Osindero Wojciech\u00a0M Czarnecki Jeff Donahue Ali Razavi Oriol Vinyals Tim Green Iain Dunning Karen Simonyan 2017. Population based Training of Neural Networks. arXiv preprint arXiv:1711.09846(2017).  Max Jaderberg Valentin Dalibard Simon Osindero Wojciech\u00a0M Czarnecki Jeff Donahue Ali Razavi Oriol Vinyals Tim Green Iain Dunning Karen Simonyan 2017. Population based Training of Neural Networks. arXiv preprint arXiv:1711.09846(2017)."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2017.2764882"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(98)00023-X"},{"key":"e_1_3_2_1_40_1","unstructured":"Kaspersky. 2021. BlackEnergy APT Attacks in Ukraine. https:\/\/www.kaspersky.com\/resource-center\/threats\/blackenergy.  Kaspersky. 2021. BlackEnergy APT Attacks in Ukraine. https:\/\/www.kaspersky.com\/resource-center\/threats\/blackenergy."},{"key":"e_1_3_2_1_41_1","unstructured":"Adrian Kelly Aidan O\u2019Sullivan Patrick de Mars and Antoine Marot. 2020. Reinforcement Learning for Electricity Network Operation. arXiv preprint arXiv:2003.07339(2020).  Adrian Kelly Aidan O\u2019Sullivan Patrick de Mars and Antoine Marot. 2020. Reinforcement Learning for Electricity Network Operation. arXiv preprint arXiv:2003.07339(2020)."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2021.3082543"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2018.2878570"},{"key":"e_1_3_2_1_44_1","unstructured":"Matthew Landen. 2022. Dragon Hyperparameters. https:\/\/github.com\/mlanden\/Dragon-Hyperparameters.  Matthew Landen. 2022. Dragon Hyperparameters. https:\/\/github.com\/mlanden\/Dragon-Hyperparameters."},{"key":"e_1_3_2_1_45_1","volume-title":"Dragos Inc.","author":"Lee M","year":"2017","unstructured":"Robert\u00a0 M Lee , MJ Assante , and T Conway . 2017 . Crashoverride: Analysis of the threat to electric grid operations . Dragos Inc. , March (2017). Robert\u00a0M Lee, MJ Assante, and T Conway. 2017. Crashoverride: Analysis of the threat to electric grid operations. Dragos Inc., March (2017)."},{"key":"e_1_3_2_1_46_1","volume-title":"A System for Massively Parallel Hyperparameter Tuning. arXiv preprint","author":"Li Liam","year":"2018","unstructured":"Liam Li , Kevin Jamieson , Afshin Rostamizadeh , Ekaterina Gonina , Moritz Hardt , Ben Recht , and Ameet Talwalkar . 2018. A System for Massively Parallel Hyperparameter Tuning. arXiv preprint ( 2018 ). Liam Li, Kevin Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Moritz Hardt, Ben Recht, and Ameet Talwalkar. 2018. A System for Massively Parallel Hyperparameter Tuning. arXiv preprint (2018)."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/1952982.1952995"},{"key":"e_1_3_2_1_48_1","unstructured":"Jason Lu. 2020. Lujasone\/ neurips_2020_l2rpn_comp_an_approach: The implementation of neurips_2020_l2rpn_track1 (robustness) and Track2 (adaptability) competition. https:\/\/github.com\/lujasone\/NeurIPS_2020_L2RPN_Comp_An_Approach.  Jason Lu. 2020. Lujasone\/ neurips_2020_l2rpn_comp_an_approach: The implementation of neurips_2020_l2rpn_track1 (robustness) and Track2 (adaptability) competition. https:\/\/github.com\/lujasone\/NeurIPS_2020_L2RPN_Comp_An_Approach."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3129487"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/SAI.2014.6918252"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1049\/el.2014.2897"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2017.8206245"},{"key":"e_1_3_2_1_53_1","unstructured":"Antoine Marot Benjamin Donnot Gabriel Dulac-Arnold Adrian Kelly A\u00efdan O\u2019Sullivan Jan Viebahn Mariette Awad Isabelle Guyon Patrick Panciatici and Camilo Romero. 2021. Learning to run a Power Network Challenge: a Retrospective Analysis. arXiv preprint arXiv:2103.03104(2021).  Antoine Marot Benjamin Donnot Gabriel Dulac-Arnold Adrian Kelly A\u00efdan O\u2019Sullivan Jan Viebahn Mariette Awad Isabelle Guyon Patrick Panciatici and Camilo Romero. 2021. Learning to run a Power Network Challenge: a Retrospective Analysis. arXiv preprint arXiv:2103.03104(2021)."},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2020.106635"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"crossref","unstructured":"A Marot B Donnot S Tazi and P Panciatici. 2018. Expert System for Topological Remedial Action Discovery in Smart Grids. (2018).  A Marot B Donnot S Tazi and P Panciatici. 2018. Expert System for Topological Remedial Action Discovery in Smart Grids. (2018).","DOI":"10.1049\/cp.2018.1875"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2014.23043"},{"key":"e_1_3_2_1_57_1","unstructured":"MITRE. 2021. ATT&CK\u00ae for Industrial Control Systems. https:\/\/collaborate.mitre.org\/attackics.  MITRE. 2021. ATT&CK\u00ae for Industrial Control Systems. https:\/\/collaborate.mitre.org\/attackics."},{"key":"e_1_3_2_1_58_1","volume-title":"Proceedings of the International Conference on Machine Learning. PMLR","author":"Mnih Volodymyr","year":"2016","unstructured":"Volodymyr Mnih , Adria\u00a0Puigdomenech Badia , Mehdi Mirza , Alex Graves , Timothy Lillicrap , Tim Harley , David Silver , and Koray Kavukcuoglu . 2016 . Asynchronous Methods for Deep Reinforcement Learning . In Proceedings of the International Conference on Machine Learning. PMLR , 1928\u20131937. Volodymyr Mnih, Adria\u00a0Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. 2016. Asynchronous Methods for Deep Reinforcement Learning. In Proceedings of the International Conference on Machine Learning. PMLR, 1928\u20131937."},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPEA53519.2022.9744697"},{"key":"e_1_3_2_1_60_1","unstructured":"Arun Nair Praveen Srinivasan Sam Blackwell Cagdas Alcicek Rory Fearon Alessandro De\u00a0Maria Vedavyas Panneershelvam Mustafa Suleyman Charles Beattie Stig Petersen 2015. Massively Parallel Methods for Deep Reinforcement Learning. arXiv preprint arXiv:1507.04296(2015).  Arun Nair Praveen Srinivasan Sam Blackwell Cagdas Alcicek Rory Fearon Alessandro De\u00a0Maria Vedavyas Panneershelvam Mustafa Suleyman Charles Beattie Stig Petersen 2015. Massively Parallel Methods for Deep Reinforcement Learning. arXiv preprint arXiv:1507.04296(2015)."},{"key":"e_1_3_2_1_61_1","unstructured":"Thanh\u00a0Thi Nguyen and Vijay\u00a0Janapa Reddi. 2019. Deep reinforcement learning for cyber security. arXiv preprint arXiv:1906.05799(2019).  Thanh\u00a0Thi Nguyen and Vijay\u00a0Janapa Reddi. 2019. Deep reinforcement learning for cyber security. arXiv preprint arXiv:1906.05799(2019)."},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/PowerTech46648.2021.9494982"},{"key":"e_1_3_2_1_63_1","unstructured":"Palo Alto Networks. 2021. Next-Generation Firewalls. Palo Alto Networks website. https:\/\/www.paloaltonetworks.com\/network-security\/next-generation-firewall.  Palo Alto Networks. 2021. Next-Generation Firewalls. Palo Alto Networks website. https:\/\/www.paloaltonetworks.com\/network-security\/next-generation-firewall."},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2015.2409775"},{"key":"e_1_3_2_1_65_1","unstructured":"Paul\u00a0W Parfomak. 2014. Physical Security of the US Power Grid: High-voltage Transformer Substations.  Paul\u00a0W Parfomak. 2014. Physical Security of the US Power Grid: High-voltage Transformer Substations."},{"key":"e_1_3_2_1_66_1","unstructured":"Colin Parris. [n. d.]. U.S. Department of Energy Recognizes GE\u2019s Work with Digital Twins. https:\/\/www.ge.com\/digital\/blog\/us-department-energy-recognizes-ges-work-digital-twins.  Colin Parris. [n. d.]. U.S. Department of Energy Recognizes GE\u2019s Work with Digital Twins. https:\/\/www.ge.com\/digital\/blog\/us-department-energy-recognizes-ges-work-digital-twins."},{"key":"e_1_3_2_1_67_1","volume-title":"PyTorch: An Imperative Style","author":"Paszke Adam","unstructured":"Adam Paszke , Sam Gross , Francisco Massa , Adam Lerer , James Bradbury , Gregory Chanan , Trevor Killeen , Zeming Lin , Natalia Gimelshein , Luca Antiga , Alban Desmaison , Andreas Kopf , Edward Yang , Zachary DeVito , Martin Raison , Alykhan Tejani , Sasank Chilamkurthy , Benoit Steiner , Lu Fang , Junjie Bai , and Soumith Chintala . 2019. PyTorch: An Imperative Style , High-Performance Deep Learning Library . In Advances in Neural Information Processing Systems 32, H.\u00a0Wallach, H.\u00a0Larochelle, A.\u00a0Beygelzimer, F.\u00a0d'Alch\u00e9-Buc, E.\u00a0Fox, and R.\u00a0Garnett (Eds.). Curran Associates, Inc., 8024\u20138035. http:\/\/papers.neurips.cc\/paper\/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems 32, H.\u00a0Wallach, H.\u00a0Larochelle, A.\u00a0Beygelzimer, F.\u00a0d'Alch\u00e9-Buc, E.\u00a0Fox, and R.\u00a0Garnett (Eds.). Curran Associates, Inc., 8024\u20138035. http:\/\/papers.neurips.cc\/paper\/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf"},{"key":"e_1_3_2_1_68_1","volume-title":"Intrusion Detection System of Industrial Control Networks using Network Telemetry","author":"Ponomarev Stanislav","unstructured":"Stanislav Ponomarev . 2015. Intrusion Detection System of Industrial Control Networks using Network Telemetry . Louisiana Tech University . Stanislav Ponomarev. 2015. Intrusion Detection System of Industrial Control Networks using Network Telemetry. Louisiana Tech University."},{"key":"e_1_3_2_1_69_1","volume-title":"Data Programming: Creating Large Training Sets, Quickly. Advances in Neural Information Processing Systems 29","author":"Ratner J","year":"2016","unstructured":"Alexander\u00a0 J Ratner , Christopher\u00a0 M De\u00a0Sa , Sen Wu , Daniel Selsam , and Christopher R\u00e9 . 2016 . Data Programming: Creating Large Training Sets, Quickly. Advances in Neural Information Processing Systems 29 (2016). Alexander\u00a0J Ratner, Christopher\u00a0M De\u00a0Sa, Sen Wu, Daniel Selsam, and Christopher R\u00e9. 2016. Data Programming: Creating Large Training Sets, Quickly. Advances in Neural Information Processing Systems 29 (2016)."},{"key":"e_1_3_2_1_70_1","unstructured":"R\u00e9seau de Transport d\u2019\u00c9lectricit\u00e9. 2022. L2RPN Challenge. https:\/\/l2rpn.chalearn.org\/.  R\u00e9seau de Transport d\u2019\u00c9lectricit\u00e9. 2022. L2RPN Challenge. https:\/\/l2rpn.chalearn.org\/."},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1109\/icSmartGrid55722.2022.9848574"},{"key":"e_1_3_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2005.857931"},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2019.23462"},{"key":"e_1_3_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1109\/ANTS.2016.7947865"},{"key":"e_1_3_2_1_75_1","volume-title":"Crashoverride: Reassessing the 2016 ukraine electric power event as a protection-focused attack. Dragos","author":"Slowik Joe","year":"2019","unstructured":"Joe Slowik . 2019 . Crashoverride: Reassessing the 2016 ukraine electric power event as a protection-focused attack. Dragos , Inc ( 2019). Joe Slowik. 2019. Crashoverride: Reassessing the 2016 ukraine electric power event as a protection-focused attack. Dragos, Inc (2019)."},{"key":"e_1_3_2_1_76_1","unstructured":"Joe Slowik. 2020. Stuxnet to Crashoverride to Trisis: Evaluating the History and Future of Integrity-Based Attacks on Industrial Environments. https:\/\/www.dragos.com\/resource\/stuxnet-to-crashoverride-to-trisis-evaluating-the-history-and-future-of-integrity-based-attacks-on-industrial-environments\/.  Joe Slowik. 2020. Stuxnet to Crashoverride to Trisis: Evaluating the History and Future of Integrity-Based Attacks on Industrial Environments. https:\/\/www.dragos.com\/resource\/stuxnet-to-crashoverride-to-trisis-evaluating-the-history-and-future-of-integrity-based-attacks-on-industrial-environments\/."},{"key":"e_1_3_2_1_77_1","volume-title":"Proceedings of the USENIX Security Symposium. 15\u201332","author":"Soltan Saleh","year":"2018","unstructured":"Saleh Soltan , Prateek Mittal , and H\u00a0Vincent Poor . 2018 . BlackIoT: IoT Botnet of High Wattage Devices can Disrupt the Power Grid . In Proceedings of the USENIX Security Symposium. 15\u201332 . Saleh Soltan, Prateek Mittal, and H\u00a0Vincent Poor. 2018. BlackIoT: IoT Botnet of High Wattage Devices can Disrupt the Power Grid. In Proceedings of the USENIX Security Symposium. 15\u201332."},{"key":"e_1_3_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.4108\/eai.1-2-2017.152155"},{"key":"e_1_3_2_1_79_1","unstructured":"Adam Stooke and Pieter Abbeel. 2018. Accelerated Methods for Deep Reinforcement Learning. arXiv preprint arXiv:1803.02811(2018).  Adam Stooke and Pieter Abbeel. 2018. Accelerated Methods for Deep Reinforcement Learning. arXiv preprint arXiv:1803.02811(2018)."},{"key":"e_1_3_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP51992.2021.00034"},{"key":"e_1_3_2_1_81_1","volume-title":"Reinforcement learning: An introduction","author":"Sutton S","unstructured":"Richard\u00a0 S Sutton and Andrew\u00a0 G Barto . 2018. Reinforcement learning: An introduction . MIT press . Richard\u00a0S Sutton and Andrew\u00a0G Barto. 2018. Reinforcement learning: An introduction. MIT press."},{"key":"e_1_3_2_1_82_1","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978388"},{"key":"e_1_3_2_1_83_1","doi-asserted-by":"publisher","DOI":"10.1109\/THS.2009.5168010"},{"key":"e_1_3_2_1_84_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2012.2224144"},{"key":"e_1_3_2_1_85_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10295"},{"key":"e_1_3_2_1_86_1","unstructured":"Heidi Vella. 2014. Under threat: protecting substations and power lines from attack. https:\/\/www.power-technology.com\/features\/featureunder-threat-protecting-substations-and-power-lines-from-attack-4192867\/.  Heidi Vella. 2014. Under threat: protecting substations and power lines from attack. https:\/\/www.power-technology.com\/features\/featureunder-threat-protecting-substations-and-power-lines-from-attack-4192867\/."},{"key":"e_1_3_2_1_87_1","doi-asserted-by":"publisher","DOI":"10.1109\/PESGM.2018.8586334"},{"key":"e_1_3_2_1_88_1","volume-title":"Power generation, operation, and control","author":"Wood J","unstructured":"Allen\u00a0 J Wood , Bruce\u00a0 F Wollenberg , and Gerald\u00a0 B Shebl\u00e9 . 2013. Power generation, operation, and control . John Wiley & Sons . Allen\u00a0J Wood, Bruce\u00a0F Wollenberg, and Gerald\u00a0B Shebl\u00e9. 2013. Power generation, operation, and control. John Wiley & Sons."},{"key":"e_1_3_2_1_89_1","volume-title":"Proceedings of the USENIX Security Symposium.","author":"Wu Xian","year":"2021","unstructured":"Xian Wu , Wenbo Guo , Hua Wei , and Xinyu Xing . 2021 . Adversarial Policy Training against Deep Reinforcement Learning . In Proceedings of the USENIX Security Symposium. Xian Wu, Wenbo Guo, Hua Wei, and Xinyu Xing. 2021. Adversarial Policy Training against Deep Reinforcement Learning. In Proceedings of the USENIX Security Symposium."},{"key":"e_1_3_2_1_90_1","doi-asserted-by":"publisher","DOI":"10.1109\/CNS.2018.8433146"},{"key":"e_1_3_2_1_91_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRD.2014.2300099"},{"key":"e_1_3_2_1_92_1","first-page":"313","article-title":"Burst-based Anomaly Detection on the DNP3 Protocol","volume":"6","author":"Yun Jeong-Han","year":"2013","unstructured":"Jeong-Han Yun , Sung-Ho Jeon , Kyoung-Ho Kim , and Woo-Nyon Kim . 2013 . Burst-based Anomaly Detection on the DNP3 Protocol . International Journal of Control and Automation 6 , 2 (2013), 313 \u2013 324 . Jeong-Han Yun, Sung-Ho Jeon, Kyoung-Ho Kim, and Woo-Nyon Kim. 2013. Burst-based Anomaly Detection on the DNP3 Protocol. International Journal of Control and Automation 6, 2 (2013), 313\u2013324.","journal-title":"International Journal of Control and Automation"},{"key":"e_1_3_2_1_93_1","unstructured":"Chiyuan Zhang Oriol Vinyals Remi Munos and Samy Bengio. 2018. A Study on Overfitting in Deep Reinforcement Learning. arXiv preprint arXiv:1804.06893(2018).  Chiyuan Zhang Oriol Vinyals Remi Munos and Samy Bengio. 2018. A Study on Overfitting in Deep Reinforcement Learning. arXiv preprint arXiv:1804.06893(2018)."}],"event":{"name":"ACSAC: Annual Computer Security Applications Conference","location":"Austin TX USA","acronym":"ACSAC"},"container-title":["Proceedings of the 38th Annual Computer Security Applications Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3564625.3567969","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3564625.3567969","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3564625.3567969","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:12Z","timestamp":1750183752000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3564625.3567969"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,5]]},"references-count":92,"alternative-id":["10.1145\/3564625.3567969","10.1145\/3564625"],"URL":"https:\/\/doi.org\/10.1145\/3564625.3567969","relation":{},"subject":[],"published":{"date-parts":[[2022,12,5]]},"assertion":[{"value":"2022-12-05","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}