Attack on Grid Event Cause Analysis: An Adversarial Machine Learning Approach

Niazazari, Iman; Livani, Hanif

doi:10.1109/isgt45199.2020.9087649

Cited by 25 publications

(13 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Related Workmentioning

confidence: 99%

“…As such, there are several recent proposals in the literature focusing on adversarial distortion of power signals generated in the smart grid [7], [12]- [14]. Niazazari and Livani [14] directly apply the technique proposed in [6] for attacking power event diagnostics ML models. Zhou et al [7] propose white-box attacks on regression ML models designed for power grid load prediction which is a modified version of [6].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Stealthy Black-Box Attacks on Deep Learning Non-Intrusive Load Monitoring Models

Wang

Srikantha

2021

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

With the advent of the advanced metering infrastructure, electricity usage data is being continuously generated at large volumes by smart meters vastly deployed across the modern power grid. Electric power utility companies and third party entities such as smart home management solution providers gain significant insights into these datasets via machine learning (ML) models. These are then utilized to perform active/passive power demand management that fosters economical and sustainable electricity usage. Although ML models are powerful, these remain vulnerable to adversarial attacks. A novel stealthy black-box attack construction model is proposed that targets deep learning models utilized to perform non-intrusive load monitoring based on smart meter data. These attacks are practical as there is no assumption of the knowledge of training data, internal parameters, and architecture of the targeted ML model. The profound impact of the proposed stealthy attack constructions on energy analytics and decision-making processes is shown through comprehensive theoretical, practical, and comparative analysis. This work sheds light on vulnerabilities of ML models in the smart grid context and provides valuable insights for securely accommodating increasing prevalence of artificial intelligence in the modern power grid.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Stealthy Black-Box Attacks on Deep Learning Non-Intrusive Load Monitoring Models

Wang

Srikantha

2021

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

show abstract

“…Niazazari and Livani [10] performed attacks on a multiclass Convolutional Neural Network (CNN) trained on simulated data. The targeted model classifies power grid events such as line energization, capacitor bank energization, or fault.…”

Section: Defense Approachesmentioning

confidence: 99%

“…Different schemes [10][11][12] have been proposed recently to defend ML algorithms against adversarial examples. Studies [10,11] made use of adversarial training.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detecting and Mitigating Adversarial Examples in Regression Tasks: A Photovoltaic Power Generation Forecasting Case Study

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Targeted adversarial attacks on wind power forecasts

Heinrich,

Scholz,

Vogt

et al. 2023

Mach Learn

View full text Add to dashboard Cite

In recent years, researchers proposed a variety of deep learning models for wind power forecasting. These models predict the wind power generation of wind farms or entire regions more accurately than traditional machine learning algorithms or physical models. However, latest research has shown that deep learning models can often be manipulated by adversarial attacks. Since wind power forecasts are essential for the stability of modern power systems, it is important to protect them from this threat. In this work, we investigate the vulnerability of two different forecasting models to targeted, semi-targeted, and untargeted adversarial attacks. We consider a long short-term memory (LSTM) network for predicting the power generation of individual wind farms and a convolutional neural network (CNN) for forecasting the wind power generation throughout Germany. Moreover, we propose the Total Adversarial Robustness Score (TARS), an evaluation metric for quantifying the robustness of regression models to targeted and semi-targeted adversarial attacks. It assesses the impact of attacks on the model’s performance, as well as the extent to which the attacker’s goal was achieved, by assigning a score between 0 (very vulnerable) and 1 (very robust). In our experiments, the LSTM forecasting model was fairly robust and achieved a TARS value of over 0.78 for all adversarial attacks investigated. The CNN forecasting model only achieved TARS values below 0.10 when trained ordinarily, and was thus very vulnerable. Yet, its robustness could be significantly improved by adversarial training, which always resulted in a TARS above 0.46.

show abstract

Attack on Grid Event Cause Analysis: An Adversarial Machine Learning Approach

Cited by 25 publications

References 12 publications

Stealthy Black-Box Attacks on Deep Learning Non-Intrusive Load Monitoring Models

Stealthy Black-Box Attacks on Deep Learning Non-Intrusive Load Monitoring Models

Detecting and Mitigating Adversarial Examples in Regression Tasks: A Photovoltaic Power Generation Forecasting Case Study

Targeted adversarial attacks on wind power forecasts

Contact Info

Product

Resources

About