Detecting and Mitigating Adversarial Examples in Regression Tasks: A Photovoltaic Power Generation Forecasting Case Study
With data collected by Internet of Things sensors, deep learning (DL) models can forecast the generation capacity of photovoltaic (PV) power plants. This functionality is especially relevant for PV power operators and users as PV plants exhibit irregular behavior related to environmental conditions....
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/12/10/394 |
| _version_ | 1797514300384870400 |
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| author | Everton Jose Santana Ricardo Petri Silva Bruno Bogaz Zarpelão Sylvio Barbon Junior |
| author_facet | Everton Jose Santana Ricardo Petri Silva Bruno Bogaz Zarpelão Sylvio Barbon Junior |
| author_sort | Everton Jose Santana |
| collection | DOAJ |
| description | With data collected by Internet of Things sensors, deep learning (DL) models can forecast the generation capacity of photovoltaic (PV) power plants. This functionality is especially relevant for PV power operators and users as PV plants exhibit irregular behavior related to environmental conditions. However, DL models are vulnerable to adversarial examples, which may lead to increased predictive error and wrong operational decisions. This work proposes a new scheme to detect adversarial examples and mitigate their impact on DL forecasting models. This approach is based on one-class classifiers and features extracted from the data inputted to the forecasting models. Tests were performed using data collected from a real-world PV power plant along with adversarial samples generated by the Fast Gradient Sign Method under multiple attack patterns and magnitudes. One-class Support Vector Machine and Local Outlier Factor were evaluated as detectors of attacks to Long-Short Term Memory and Temporal Convolutional Network forecasting models. According to the results, the proposed scheme showed a high capability of detecting adversarial samples with an average F1-score close to 90%. Moreover, the detection and mitigation approach strongly reduced the prediction error increase caused by adversarial samples. |
| first_indexed | 2024-03-10T06:29:56Z |
| format | Article |
| id | doaj.art-8dcd3143098d45b395eba3a506ddb04b |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-10T06:29:56Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-8dcd3143098d45b395eba3a506ddb04b2023-11-22T18:37:27ZengMDPI AGInformation2078-24892021-09-01121039410.3390/info12100394Detecting and Mitigating Adversarial Examples in Regression Tasks: A Photovoltaic Power Generation Forecasting Case StudyEverton Jose Santana0Ricardo Petri Silva1Bruno Bogaz Zarpelão2Sylvio Barbon Junior3Department of Software Engineering, Pontifical Catholic University of Paraná, Londrina 86067000, BrazilDepartment of Electrical Engineering, State University of Londrina, Londrina 86057970, BrazilDepartment of Computer Science, State University of Londrina, Londrina 86057970, BrazilDepartment of Computer Science, State University of Londrina, Londrina 86057970, BrazilWith data collected by Internet of Things sensors, deep learning (DL) models can forecast the generation capacity of photovoltaic (PV) power plants. This functionality is especially relevant for PV power operators and users as PV plants exhibit irregular behavior related to environmental conditions. However, DL models are vulnerable to adversarial examples, which may lead to increased predictive error and wrong operational decisions. This work proposes a new scheme to detect adversarial examples and mitigate their impact on DL forecasting models. This approach is based on one-class classifiers and features extracted from the data inputted to the forecasting models. Tests were performed using data collected from a real-world PV power plant along with adversarial samples generated by the Fast Gradient Sign Method under multiple attack patterns and magnitudes. One-class Support Vector Machine and Local Outlier Factor were evaluated as detectors of attacks to Long-Short Term Memory and Temporal Convolutional Network forecasting models. According to the results, the proposed scheme showed a high capability of detecting adversarial samples with an average F1-score close to 90%. Moreover, the detection and mitigation approach strongly reduced the prediction error increase caused by adversarial samples.https://www.mdpi.com/2078-2489/12/10/394intelligent cyber-physical systemsadversarial machine learningsmart gridsecurityphotovoltaic generation forecast |
| spellingShingle | Everton Jose Santana Ricardo Petri Silva Bruno Bogaz Zarpelão Sylvio Barbon Junior Detecting and Mitigating Adversarial Examples in Regression Tasks: A Photovoltaic Power Generation Forecasting Case Study Information intelligent cyber-physical systems adversarial machine learning smart grid security photovoltaic generation forecast |
| title | Detecting and Mitigating Adversarial Examples in Regression Tasks: A Photovoltaic Power Generation Forecasting Case Study |
| title_full | Detecting and Mitigating Adversarial Examples in Regression Tasks: A Photovoltaic Power Generation Forecasting Case Study |
| title_fullStr | Detecting and Mitigating Adversarial Examples in Regression Tasks: A Photovoltaic Power Generation Forecasting Case Study |
| title_full_unstemmed | Detecting and Mitigating Adversarial Examples in Regression Tasks: A Photovoltaic Power Generation Forecasting Case Study |
| title_short | Detecting and Mitigating Adversarial Examples in Regression Tasks: A Photovoltaic Power Generation Forecasting Case Study |
| title_sort | detecting and mitigating adversarial examples in regression tasks a photovoltaic power generation forecasting case study |
| topic | intelligent cyber-physical systems adversarial machine learning smart grid security photovoltaic generation forecast |
| url | https://www.mdpi.com/2078-2489/12/10/394 |
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