A Deep Learning-Based Classification Scheme for False Data Injection Attack Detection in Power System
Abstract
:1. Introduction
- The standard DBN is improved to deal with the continuous real-time series data of the power system flexibly and extract the time correlation.
- A CDBN-based FDIA detection scheme is proposed to evaluate the reliability of the measurement and ensure the safe and stable operation of the power grid.
- By simulating different attack scenarios, the performance of the proposed scheme is evaluated from multiple aspects to ensure its feasibility and effectiveness.
2. System Model
2.1. State Estimation in Power Systems
2.2. Conventional Bad Data Detection
3. False Data Injection Attack
- Least-effort attack [19]: k = 1, adversaries manipulate the minimum number of measurements to launch the FDIA;
4. Deep Learning-Based Identification Scheme
4.1. Conventional RBM
4.2. Conditional Gaussian-Bernoulli RBM
4.3. CDBN
5. Simulation
5.1. Experimental Results
5.1.1. Structural Design
- I.
- Effect of the height and width of the CDBN
- II.
- Effect of the Observation Window Structure
5.1.2. Multi-Scenario Validation
5.1.3. Robustness Validation
5.2. Analysis of Results
- The choice of the parameters
- The presence of environmental noise
- Insufficient data
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Nomenclature
ANN | Artificial neural network |
AUC | Area under curve |
BDD | Bad data detection |
CDBN | Conditional deep belief network |
CGBRBM | Conditional Gaussian-Bernoulli RBM |
CD | Contrast divergence |
DOID | Distributed observable island detection |
DTAD | Distributed time approaching detection |
DBN | Deep belief network |
FDIA | False data injection attack |
RBM | Restricted boltzmann machine |
ROC | Receiver operating characteristics |
SVM | Support vector machine |
SCADA | Supervisory control and data acquisition system |
Attack vector | |
, | Standard biases |
Dynamic biases from the past to the visible bias vector | |
Weight matrices of the kth previous visible vector to the current visible unit | |
Elements of | |
Weight matrices of the kth previous visible vector to the current hidden unit | |
Elements of | |
Dynamic biases from the past to the hidden bias vector | |
Arbitrary vector added to the state variable | |
Measurement error vector | |
Jacobian matrix | |
H | Number of elements in each layer of CDBN |
State of hidden unit j | |
h(∙) | Nonlinear relationship between the measurement z and the state x |
I | Time interval between two adjacent time steps |
k | Number of attacked measurements |
Predicted value | |
Actual value | |
N | Size of the observation window at the previous time |
Gaussian with mean µ and variance σ2 | |
n, m | Numbers of visible and hidden units |
ith activation probability of the (k − 1)th hidden layer | |
jth activation probability of the kth hidden layer | |
Activation probability of the output layer | |
State of visible unit i | |
ith real-valued visible element at time step t | |
Weight between unit i and unit j | |
jhth element of the (k + 1)th layer weight matrix | |
State vector | |
Compromised state vector | |
Compromised vector of all measurements | |
Vector of all measurements | |
Threshold of BDD system | |
Learning rate | |
Expectations calculated from the data | |
Expectations calculated from the model distributions | |
Standard deviation of the ith visible element |
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Ding, Y.; Ma, K.; Pu, T.; Wang, X.; Li, R.; Zhang, D. A Deep Learning-Based Classification Scheme for False Data Injection Attack Detection in Power System. Electronics 2021, 10, 1459. https://doi.org/10.3390/electronics10121459
Ding Y, Ma K, Pu T, Wang X, Li R, Zhang D. A Deep Learning-Based Classification Scheme for False Data Injection Attack Detection in Power System. Electronics. 2021; 10(12):1459. https://doi.org/10.3390/electronics10121459
Chicago/Turabian StyleDing, Yucheng, Kang Ma, Tianjiao Pu, Xinying Wang, Ran Li, and Dongxia Zhang. 2021. "A Deep Learning-Based Classification Scheme for False Data Injection Attack Detection in Power System" Electronics 10, no. 12: 1459. https://doi.org/10.3390/electronics10121459
APA StyleDing, Y., Ma, K., Pu, T., Wang, X., Li, R., & Zhang, D. (2021). A Deep Learning-Based Classification Scheme for False Data Injection Attack Detection in Power System. Electronics, 10(12), 1459. https://doi.org/10.3390/electronics10121459