research-article

Understanding Credibility of Adversarial Examples against Smart Grid: A Case Study for Voltage Stability Assessment

Authors:

Yan XuAuthors Info & Claims

e-Energy '21: Proceedings of the Twelfth ACM International Conference on Future Energy Systems

Pages 95 - 106

https://doi.org/10.1145/3447555.3464859

Published: 22 June 2021 Publication History

Abstract

Stability assessment is an important task for maintaining reliable operations of power grids. With increased system complexity, deep learning-based stability assessment approaches are promising to address the shortfalls of the traditional time-domain simulation-based approaches. However, in the field of computer vision, the deep learning models are shown vulnerable to adversarial examples. Although this vulnerability has been noticed by the energy informatics research, the domain-specific analysis on the requirements imposed for implementing effective adversarial examples is still lacking. These attack requirements, albeit reasonable in computer vision tasks, can be too stringent in the context of power grids. In this paper, we systematically investigate the requirements and discuss the credibility of six representative adversarial example attacks for a case study of voltage stability assessment for the New England 10-machine 39-bus system. We show that (1) compromising the voltage traces of half of transmission system buses is a rule of thumb requirement; (2) the universal adversarial perturbations that are independent of the original clean voltage trajectory have the same credibility as the widely studied false data injection attacks on power grid state estimation, while other adversarial example attacks are less credible; (3) the universal perturbations can be effectively defended with strong adversarial training.

References

[1]

[n.d.]. Final Report on the August 14, 2003 Blackout in the United States and Canada: Causes and Recommendations. https://www.energy.gov/sites/prod/files/oeprod/DocumentsandMedia/BlackoutFinal-Web.pdf. Accessed: 2020-12-11.

[2]

[n.d.]. Tencent Keen Security Lab: Experimental Security Research of Tesla Autopilot. https://keenlab.tencent.com/en/2019/03/29/Tencent-Keen-Security-Lab-Experimental-Security-Research-of-Tesla-Autopilot/.

[3]

Naveed Akhtar and Ajmal Mian. 2018. Threat of adversarial attacks on deep learning in computer vision: A survey. IEEE Access 6 (2018), 14410--14430.

[4]

T Athay, R Podmore, and S Virmani. 1979. A practical method for the direct analysis of transient stability. IEEE Transactions on Power Apparatus and Systems 2 (1979), 573--584.

[5]

Suzhi Bi and Ying Jun Zhang. 2014. Graphical methods for defense against false-data injection attacks on power system state estimation. Transactions on Smart Grid 5, 3 (2014), 1216--1227.

[6]

Nicholas Carlini and David Wagner. 2017. Towards evaluating the robustness of neural networks. In Proceedings of the IEEE Symposium on Security and Privacy.

[7]

Yize Chen, Yushi Tan, and Baosen Zhang. 2019. Exploiting vulnerabilities of load forecasting through adversarial attacks. In Proceedings of the ACM International Conference on Future Energy Systems.

Digital Library

[8]

Sambarta Dasgupta, Magesh Paramasivam, Umesh Vaidya, and Venkataramana Ajjarapu. 2013. Real-time monitoring of short-term voltage stability using PMU data. Transactions on Power Systems 28, 4 (2013), 3702--3711.

[9]

Zhao Yang Dong, Yan Xu, Pei Zhang, and Kit Po Wong. 2013. Using IS to assess an electric power system's real-time stability. IEEE Intelligent Systems 28, 4 (2013), 60--66.

Digital Library

[10]

Mohammad Esmalifalak, Lanchao Liu, Nam Nguyen, Rong Zheng, and Zhu Han. 2014. Detecting stealthy false data injection using machine learning in smart grid. IEEE Systems Journal 11, 3 (2014), 1644--1652.

[11]

Mohammad Esmalifalak, Huy Nguyen, Rong Zheng, and Zhu Han. 2011. Stealth false data injection using independent component analysis in smart grid. In Proceedings of the IEEE International Conference on Smart Grid Communications.

[12]

Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial networks. In Proceedings of the International Conference on Neural Information Processing Systems.

[13]

Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015. Explaining and Harnessing Adversarial Examples. In Proceedings of the International Conference on Learning Representations.

[14]

Jamie Hayes and George Danezis. 2018. Learning universal adversarial perturbations with generative models. In Proceedings of the IEEE Symposium on Security and Privacy Workshop.

[15]

Qiuhua Huang, Renke Huang, Weituo Hao, Jie Tan, Rui Fan, and Zhenyu Huang. 2019. Adaptive power system emergency control using deep reinforcement learning. Transactions on Smart Grid 11, 2 (2019), 1171--1182.

[16]

JQ James, David J Hill, Albert YS Lam, Jiatao Gu, and Victor OK Li. 2017. Intelligent time-adaptive transient stability assessment system. Transactions on Power Systems 33, 1 (2017), 1049--1058.

[17]

Guoqing Jin, Shiwei Shen, Dongming Zhang, Feng Dai, and Yongdong Zhang. 2019. Ape-gan: Adversarial perturbation elimination with gan. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing.

[18]

Peizhong Ju and Xiaojun Lin. 2018. Adversarial attacks to distributed voltage control in power distribution networks with DERs. In Proceedings of the International Conference on Future Energy Systems.

Digital Library

[19]

K Kawabe and K Tanaka. 2014. Analytical method for short-term voltage stability using the stability boundary in the PV plane. Transactions on Power Systems 29, 6 (2014), 3041--3047.

[20]

Prabha Kundur, Neal J Balu, and Mark G Lauby. 1994. Power system stability and control. Vol. 7. McGraw-hill New York.

[21]

Prabha Kundur, John Paserba, Venkat Ajjarapu, Göran Andersson, Anjan Bose, Claudio Canizares, Nikos Hatziargyriou, David Hill, Alex Stankovic, Carson Taylor, et al. 2004. Definition and classification of power system stability IEEE/CIGRE joint task force on stability terms and definitions. Transactions on Power Systems 19, 3 (2004), 1387--1401.

[22]

Denis Foo Kune, John Backes, Shane S Clark, Daniel Kramer, Matthew Reynolds, Kevin Fu, Yongdae Kim, and Wenyuan Xu. 2013. Ghost talk: Mitigating EMI signal injection attacks against analog sensors. In Proceedings of the IEEE Symposium on Security and Privacy.

Digital Library

[23]

Alexey Kurakin, Ian Goodfellow, and Samy Bengio. 2017. Adversarial examples in the physical world. In Proceedings of the International Conference on Learning Representations Workshop.

[24]

Subhash Lakshminarayana, Teo Zhan Teng, David KY Yau, and Rui Tan. 2017. Optimal attack against cyber-physical control systems with reactive attack mitigation. In Proceedings of the International Conference on Future Energy Systems.

Digital Library

[25]

Jie Lin, Wei Yu, Xinyu Yang, Guobin Xu, and Wei Zhao. 2012. On false data injection attacks against distributed energy routing in smart grid. In Proceedings of the International Conference on Cyber-Physical Systems. IEEE.

Digital Library

[26]

WY Liu, BP Tang, JG Han, XN Lu, NN Hu, and ZZ He. 2015. The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review. Renewable and Sustainable Energy Reviews 44 (2015), 466--472.

[27]

Yao Liu, Peng Ning, and Michael K Reiter. 2009. False data injection attacks against state estimation in electric power grids. In Proceedings of the ACM Conference on Computer and Communications Security.

Digital Library

[28]

Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018. Towards deep learning models resistant to adversarial attacks. In Proceedings of the International Conference on Learning Representations.

[29]

Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard. 2017. Universal adversarial perturbations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

[30]

Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard. 2016. Deepfool: a simple and accurate method to fool deep neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

[31]

Michael Negnevitsky, Paras Mandal, and Anurag K Srivastava. 2009. Machine learning applications for load, price and wind power prediction in power systems. In Proceedings of the International Conference on Intelligent System Applications to Power Systems. IEEE.

[32]

Mohammad Ashiqur Rahman, Ehab Al-Shaer, and Rajesh G Kavasseri. 2014. A formal model for verifying the impact of stealthy attacks on optimal power flow in power grids. In Proceedings of the International Conference on Cyber-Physical Systems. IEEE.

Digital Library

[33]

Kui Ren, Tianhang Zheng, Zhan Qin, and Xue Liu. 2020. Adversarial attacks and defenses in deep learning. Engineering (2020).

[34]

Seunghyoung Ryu, Jaekoo Noh, and Hongseok Kim. 2017. Deep neural network based demand side short term load forecasting. Energies 10, 1 (2017), 3.

[35]

Rui Tan, Hoang Hai Nguyen, Eddy YS Foo, Xinshu Dong, David KY Yau, Zbigniew Kalbarczyk, Ravishankar K Iyer, and Hoay Beng Gooi. 2016. Optimal false data injection attack against automatic generation control in power grids. In Proceedings of the International Conference on Cyber-Physical Systems. IEEE.

Digital Library

[36]

Cihang Xie, Yuxin Wu, Laurens van der Maaten, Alan L Yuille, and Kaiming He. 2019. Feature denoising for improving adversarial robustness. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

[37]

Le Xie, Yilin Mo, and Bruno Sinopoli. 2011. Integrity data attacks in power market operations. Transactions on Smart Grid 2, 4 (2011), 659--666.

[38]

Yan Xu, Zhao Yang Dong, Jun Hua Zhao, Pei Zhang, and Kit Po Wong. 2012. A reliable intelligent system for real-time dynamic security assessment of power systems. Transactions on Power Systems 27, 3 (2012), 1253--1263.

[39]

Yinliang Xu, Wei Zhang, Wenxin Liu, and Frank Ferrese. 2012. Multiagent-based reinforcement learning for optimal reactive power dispatch. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 42, 6 (2012), 1742--1751.

Digital Library

[40]

Tao Yu, Bin Zhou, Ka Wing Chan, Liang Chen, and Bo Yang. 2011. Stochastic Optimal Relaxed Automatic Generation Control in Non-Markov Environment Based on Multi-Step Q(λ) Learning. Transactions on Power Systems 26, 3 (2011), 1272--1282.

[41]

Jianwu Zeng and Wei Qiao. 2013. Short-term solar power prediction using a support vector machine. Renewable Energy 52 (2013), 118--127.

[42]

Yuchen Zhang, Yan Xu, Zhao Yang Dong, and Rui Zhang. 2018. A hierarchical self-adaptive data-analytics method for real-time power system short-term voltage stability assessment. Transactions on Industrial Informatics 15, 1 (2018), 74--84.

Cited By

Sánchez GElbez GHagenmeyer V(2024)Attacking Learning-based Models in Smart Grids: Current Challenges and New FrontiersThe 15th ACM International Conference on Future and Sustainable Energy Systems10.1145/3632775.3661984(589-595)Online publication date: 21-May-2024
https://doi.org/10.1145/3632775.3661984
Youssef ELabeau FKassouf M(2024)Adversarial Dynamic Load-Altering Cyberattacks Against Peak Shaving Using Residential Electric Water HeatersIEEE Transactions on Smart Grid10.1109/TSG.2023.330023915:2(2073-2088)Online publication date: Mar-2024
https://doi.org/10.1109/TSG.2023.3300239
Li WDeka DWang RPaternina M(2024)Physics-Constrained Adversarial Training for Neural Networks in Stochastic Power GridsIEEE Transactions on Artificial Intelligence10.1109/TAI.2023.32363775:3(1121-1131)Online publication date: Mar-2024
https://doi.org/10.1109/TAI.2023.3236377
Show More Cited By

Index Terms

Understanding Credibility of Adversarial Examples against Smart Grid: A Case Study for Voltage Stability Assessment

Recommendations

Adversarial examples: A survey of attacks and defenses in deep learning-enabled cybersecurity systems
Abstract
Over the last few years, the adoption of machine learning in a wide range of domains has been remarkable. Deep learning, in particular, has been extensively used to drive applications and services in specializations such as computer vision, ...
Highlights
- A taxonomy of cybersecurity applications is established.
- Adversarial machine learning is systematically overviewed.
- An extensive, curated list of cybersecurity-related datasets is provided.
- Methods for generating adversarial ...
Adversarial machine learning in IoT from an insider point of view
Abstract
With the rapid progress and significant successes in various applications, machine learning has been considered a crucial component in the Internet of Things ecosystem. However, machine learning models have recently been vulnerable to ...
On Credibility of Adversarial Examples Against Learning-Based Grid Voltage Stability Assessment
Voltage stability assessment is essential for maintaining reliable power grid operations. Stability assessment approaches using deep learning address the shortfalls of the traditional time-domain simulation-based approaches caused by increased system ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

e-Energy '21: Proceedings of the Twelfth ACM International Conference on Future Energy Systems

June 2021

528 pages

ISBN:9781450383332

DOI:10.1145/3447555

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

e-Energy '21

e-Energy '21: The Twelfth ACM International Conference on Future Energy Systems

June 28 - July 2, 2021

Virtual Event, Italy

Acceptance Rates

Overall Acceptance Rate 160 of 446 submissions, 36%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

12
Total Citations
View Citations
241
Total Downloads

Downloads (Last 12 months)54
Downloads (Last 6 weeks)0

Reflects downloads up to 26 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Sánchez GElbez GHagenmeyer V(2024)Attacking Learning-based Models in Smart Grids: Current Challenges and New FrontiersThe 15th ACM International Conference on Future and Sustainable Energy Systems10.1145/3632775.3661984(589-595)Online publication date: 21-May-2024
https://doi.org/10.1145/3632775.3661984
Youssef ELabeau FKassouf M(2024)Adversarial Dynamic Load-Altering Cyberattacks Against Peak Shaving Using Residential Electric Water HeatersIEEE Transactions on Smart Grid10.1109/TSG.2023.330023915:2(2073-2088)Online publication date: Mar-2024
https://doi.org/10.1109/TSG.2023.3300239
Li WDeka DWang RPaternina M(2024)Physics-Constrained Adversarial Training for Neural Networks in Stochastic Power GridsIEEE Transactions on Artificial Intelligence10.1109/TAI.2023.32363775:3(1121-1131)Online publication date: Mar-2024
https://doi.org/10.1109/TAI.2023.3236377
Agarwal ANene M(2024)Addressing AI Risks in Critical Infrastructure: Formalising the AI Incident Reporting Process2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)10.1109/CONECCT62155.2024.10677312(1-6)Online publication date: 12-Jul-2024
https://doi.org/10.1109/CONECCT62155.2024.10677312
Wang SKo RBai GDong NChoi TZhang Y(2024)Evasion Attack and Defense on Machine Learning Models in Cyber-Physical Systems: A SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2023.334480826:2(930-966)Online publication date: Oct-2025
https://doi.org/10.1109/COMST.2023.3344808
Efatinasab EBrighente ARampazzo MAzadi NConti M(2024)GAN-GRID: A Novel Generative Attack on Smart Grid Stability PredictionComputer Security – ESORICS 202410.1007/978-3-031-70879-4_19(374-393)Online publication date: 16-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70879-4_19
Kumar ANaqvi BWolff A(2023)Exploring the energy informatics and energy citizenship domains: a systematic literature reviewEnergy Informatics10.1186/s42162-023-00268-16:1Online publication date: 2-Aug-2023
https://doi.org/10.1186/s42162-023-00268-1
Chen YSun MChu ZCamal SKariniotakis GTeng F(2023)Vulnerability and Impact of Machine Learning-Based Inertia Forecasting Under Cost-Oriented Data Integrity AttackIEEE Transactions on Smart Grid10.1109/TSG.2022.320751714:3(2275-2287)Online publication date: May-2023
https://doi.org/10.1109/TSG.2022.3207517
Bellizio FXu WQiu DYe YPapadaskalopoulos DCremer JTeng FStrbac G(2023)Transition to Digitalized Paradigms for Security Control and Decentralized Electricity MarketProceedings of the IEEE10.1109/JPROC.2022.3161053111:7(744-761)Online publication date: Jul-2023
https://doi.org/10.1109/JPROC.2022.3161053
Zhang JHu JMiao CWang Q(2023)A GAN-based Adversarial Attack Method for Data-driven State Estimation2023 IEEE 6th International Electrical and Energy Conference (CIEEC)10.1109/CIEEC58067.2023.10165834(3655-3659)Online publication date: 12-May-2023
https://doi.org/10.1109/CIEEC58067.2023.10165834
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents