Enabling Cyberattack-Resilient Load Forecasting through Adversarial Machine Learning

Zefan Tang; Jieying Jiao; Peng Zhang; Meng Yue; Chen Chen; Jun Yan

doi:10.1109/PESGM40551.2019.8974076

Enabling Cyberattack-Resilient Load Forecasting through Adversarial Machine Learning

Zefan Tang
, Jieying Jiao
, Peng Zhang
, Meng Yue
, Chen Chen
, Jun Yan

University of Connecticut
Brookhaven National Laboratory
Argonne National Laboratory

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

19 Scopus citations

Abstract

Developing cyberattack-resilient load forecasting is critical for electric utilities in the face of increasingly broad cyberattack surfaces. It is, however, a challenging task due to the adversary's unknown behaviors. This paper bridges the gap by developing an adversarial machine learning (AML) approach for cyberattack-resilient load forecasting. The novelties of this paper include: 1) its analysis of cyber security issues for traditional artificial neural network (ANN) based load forecasting; 2) the ensemble adversarial training it establishes to tackle different attack scenarios; and 3) the selection of parameters for AML it evaluates to achieve desired performance. Test results validate the effectiveness and excellent performance of the presented method.

Original language	English
Title of host publication	2019 IEEE Power and Energy Society General Meeting, PESGM 2019
Publisher	IEEE Computer Society
ISBN (Electronic)	9781728119816
DOIs	https://doi.org/10.1109/PESGM40551.2019.8974076
State	Published - Aug 2019
Event	2019 IEEE Power and Energy Society General Meeting, PESGM 2019 - Atlanta, United States Duration: Aug 4 2019 → Aug 8 2019

Publication series

Name	IEEE Power and Energy Society General Meeting
Volume	2019-August
ISSN (Print)	1944-9925
ISSN (Electronic)	1944-9933

Conference

Conference	2019 IEEE Power and Energy Society General Meeting, PESGM 2019
Country/Territory	United States
City	Atlanta
Period	08/4/19 → 08/8/19

Keywords

Load forecasting
adversarial machine learning
cyber security
ensemble adversarial training
power systems

Access to Document

10.1109/PESGM40551.2019.8974076

Cite this

Tang, Z., Jiao, J., Zhang, P., Yue, M., Chen, C., & Yan, J. (2019). Enabling Cyberattack-Resilient Load Forecasting through Adversarial Machine Learning. In 2019 IEEE Power and Energy Society General Meeting, PESGM 2019 Article 8974076 (IEEE Power and Energy Society General Meeting; Vol. 2019-August). IEEE Computer Society. https://doi.org/10.1109/PESGM40551.2019.8974076

@inproceedings{ca9db01197ce47b3b70a7a3c35b3f63d,

title = "Enabling Cyberattack-Resilient Load Forecasting through Adversarial Machine Learning",

abstract = "Developing cyberattack-resilient load forecasting is critical for electric utilities in the face of increasingly broad cyberattack surfaces. It is, however, a challenging task due to the adversary's unknown behaviors. This paper bridges the gap by developing an adversarial machine learning (AML) approach for cyberattack-resilient load forecasting. The novelties of this paper include: 1) its analysis of cyber security issues for traditional artificial neural network (ANN) based load forecasting; 2) the ensemble adversarial training it establishes to tackle different attack scenarios; and 3) the selection of parameters for AML it evaluates to achieve desired performance. Test results validate the effectiveness and excellent performance of the presented method.",

keywords = "Load forecasting, adversarial machine learning, cyber security, ensemble adversarial training, power systems",

author = "Zefan Tang and Jieying Jiao and Peng Zhang and Meng Yue and Chen Chen and Jun Yan",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Power and Energy Society General Meeting, PESGM 2019 ; Conference date: 04-08-2019 Through 08-08-2019",

year = "2019",

month = aug,

doi = "10.1109/PESGM40551.2019.8974076",

language = "English",

series = "IEEE Power and Energy Society General Meeting",

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booktitle = "2019 IEEE Power and Energy Society General Meeting, PESGM 2019",

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Tang, Z, Jiao, J, Zhang, P, Yue, M, Chen, C & Yan, J 2019, Enabling Cyberattack-Resilient Load Forecasting through Adversarial Machine Learning. in 2019 IEEE Power and Energy Society General Meeting, PESGM 2019., 8974076, IEEE Power and Energy Society General Meeting, vol. 2019-August, IEEE Computer Society, 2019 IEEE Power and Energy Society General Meeting, PESGM 2019, Atlanta, United States, 08/4/19. https://doi.org/10.1109/PESGM40551.2019.8974076

Enabling Cyberattack-Resilient Load Forecasting through Adversarial Machine Learning. / Tang, Zefan; Jiao, Jieying; Zhang, Peng et al.
2019 IEEE Power and Energy Society General Meeting, PESGM 2019. IEEE Computer Society, 2019. 8974076 (IEEE Power and Energy Society General Meeting; Vol. 2019-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Enabling Cyberattack-Resilient Load Forecasting through Adversarial Machine Learning

AU - Tang, Zefan

AU - Jiao, Jieying

AU - Zhang, Peng

AU - Yue, Meng

AU - Chen, Chen

AU - Yan, Jun

PY - 2019/8

Y1 - 2019/8

N2 - Developing cyberattack-resilient load forecasting is critical for electric utilities in the face of increasingly broad cyberattack surfaces. It is, however, a challenging task due to the adversary's unknown behaviors. This paper bridges the gap by developing an adversarial machine learning (AML) approach for cyberattack-resilient load forecasting. The novelties of this paper include: 1) its analysis of cyber security issues for traditional artificial neural network (ANN) based load forecasting; 2) the ensemble adversarial training it establishes to tackle different attack scenarios; and 3) the selection of parameters for AML it evaluates to achieve desired performance. Test results validate the effectiveness and excellent performance of the presented method.

AB - Developing cyberattack-resilient load forecasting is critical for electric utilities in the face of increasingly broad cyberattack surfaces. It is, however, a challenging task due to the adversary's unknown behaviors. This paper bridges the gap by developing an adversarial machine learning (AML) approach for cyberattack-resilient load forecasting. The novelties of this paper include: 1) its analysis of cyber security issues for traditional artificial neural network (ANN) based load forecasting; 2) the ensemble adversarial training it establishes to tackle different attack scenarios; and 3) the selection of parameters for AML it evaluates to achieve desired performance. Test results validate the effectiveness and excellent performance of the presented method.

KW - Load forecasting

KW - adversarial machine learning

KW - cyber security

KW - ensemble adversarial training

KW - power systems

UR - https://www.scopus.com/pages/publications/85079044765

U2 - 10.1109/PESGM40551.2019.8974076

DO - 10.1109/PESGM40551.2019.8974076

M3 - Conference contribution

AN - SCOPUS:85079044765

T3 - IEEE Power and Energy Society General Meeting

BT - 2019 IEEE Power and Energy Society General Meeting, PESGM 2019

PB - IEEE Computer Society

T2 - 2019 IEEE Power and Energy Society General Meeting, PESGM 2019

Y2 - 4 August 2019 through 8 August 2019

ER -

Enabling Cyberattack-Resilient Load Forecasting through Adversarial Machine Learning

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