Cyber-Attack Detection in DC Microgrids Based on Deep Machine Learning and Wavelet Singular Values Approach
Abstract
:1. Introduction
1.1. Background
1.2. Motivation and Main Contribution
- WT and SVD are combined to extract features from signals and obtained singular values (SVs) of the signals.
- A singular value of the signals has been used as input of DL to diagnose FDIA in DC-MG for the first time.
- Deep base models have been built to learn hidden properties from signals adaptively.
- Several deep base patterns are obtained with AE and bootstrap types.
- DWV is designed with particular class thresholds to perform elective ensembles.
- The DWV’s class-specific thresholds are optimized by applying the GWO algorithm.
1.3. Paper Structure
2. Basic Concepts
2.1. Wavelet Singular Values
2.1.1. Wavelet Transform
2.1.2. Singular Value Decomposition
2.2. Deep Learning Method
2.2.1. Multiple Diverse Deep Auto-Encoders Construction
2.2.2. DWV with Class-Specific Thresholds
2.2.3. Gray Wolf Optimization
2.3. Procedure of the Proposed Technique
3. CYBER PHYSICAL LAYER in DC-MGs
3.1. Cyber Security in DC-MG
3.2. Cyber-Attack
4. Simulation Outcomes
4.1. Case Studies
4.2. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Reference | Methodology | Detection Technique | Limitation | Advantages of Our Proposed Technique |
---|---|---|---|---|
[12] | Cooperative vulnerability factor | Considering attacks on measurements in DC-MG | Criteria indexes of the accuracy rate like miss rate (MR), false alarm rate (FAR), hit rate (HR), and correct reject rate (CRR) are not considered and not compared to the other FDIA detection methods based on the accuracy criteria indexes | Both voltage and current measurement are investigated in the proposed study. Criteria indexes of the accuracy rate are considered and compared to the other FDIA detection methods; The technique is free-model-based |
[24] | Kalman filter | Applying the mathematical method in smart grids to detect the FDIAs | Criteria indexes of the accuracy rate like MR, FAR, HR, and CRR are not considered and not compared to the other FDIA detection methods based on the accuracy criteria indexes; DC system is not considered; | This work is not dependent on the system’s mathematical model. Criteria indexes of the accuracy rate are considered and compared to the other FDIA detection methods; DC-MG has been investigated |
[38] | Deep learning | Applying DL schemes to identify the properties behavior of FDIA with the historical measurement information and using the captured properties to diagnose the FDIA | Criteria indexes of the accuracy rate like MR, FAR, HR, and CRR are not considered for the FDIA detection; DC system is not considered; | Applying WT and SVD to extract features to use as input of deep learning and deep base models are built to adaptively learn hidden properties. Criteria indexes of the accuracy rate are considered and compared to the other FDIA detection methods; |
[39] | Kullback–Leibler | The ability to diagnose different attacks. Has difficulty diagnosing FDIAs in some state variables | Using historical data so it cannot detect small FDIA to the system. Criteria indexes of the accuracy rate like MR, FAR, HR, and CRR are not considered for the FDIA detection; | This work is not dependent on the state variable for detection FDIAs and is based on signals features. Various criteria indexes are considered and compared. It does not depend on the historical data |
[40] | Chi-square detector and cosine similarity matching were applied | The results illustrated that detection based Chi-square could not detect the examined FDIAs | Criteria indexes of the accuracy rate like MR, FAR, HR, and CRR are not considered for the FDIA detection; and is not compared to the other FDIA detection methods based on the accuracy criteria indexes; DC system is not considered; | This paper is capable to detect various FDIAs in smart MG; Criteria indexes of the accuracy rate are considered and compared to the other FDIA detection methods; DC-MG has been investigated; |
[41] | Discordant Element Approach | Considering attacks on current measurements in DC-MG | Criteria indexes of the accuracy rate like MR, FAR, HR, and CRR are not considered for the FDIA detection; and not compared to the other FDIA detection methods based on the accuracy criteria indexes; does not consider the attack on voltage sensors | Both voltage and current measurement are investigated in the proposed study. Criteria indexes of the accuracy rate are considered and compared to the other FDIA detection methods; |
Parameter | Value |
---|---|
Type | Real Value | ||
---|---|---|---|
Detection Type Response | Form | Positives | Negatives |
Positives | Hit Rate True Positive | False Alarm Rate False Positive | |
Negatives | Miss Rate False Negative | Correct Rejection Rate True Negative |
Type | Method | Real Value | ||
---|---|---|---|---|
Form | Positives | Negatives | ||
Detection Type Response | WT and DL | Positives | 95.36% | 4.16% |
Negatives | 4.64% | 95.84% | ||
Shallow Model | Positives | 90.13% | 8.36% | |
Negatives | 9.87% | 91.64% | ||
HHT and DL [48] | Positives | 93.75% | 4.77% | |
Negatives | 6.25% | 95.23% | ||
Method | WT and DL | HHT and DL [48] | ||
Response Time | Average Detection Time | 10 ms | 50 ms | |
DNN Training Time | 1638 s | 1759 s |
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Dehghani, M.; Niknam, T.; Ghiasi, M.; Bayati, N.; Savaghebi, M. Cyber-Attack Detection in DC Microgrids Based on Deep Machine Learning and Wavelet Singular Values Approach. Electronics 2021, 10, 1914. https://doi.org/10.3390/electronics10161914
Dehghani M, Niknam T, Ghiasi M, Bayati N, Savaghebi M. Cyber-Attack Detection in DC Microgrids Based on Deep Machine Learning and Wavelet Singular Values Approach. Electronics. 2021; 10(16):1914. https://doi.org/10.3390/electronics10161914
Chicago/Turabian StyleDehghani, Moslem, Taher Niknam, Mohammad Ghiasi, Navid Bayati, and Mehdi Savaghebi. 2021. "Cyber-Attack Detection in DC Microgrids Based on Deep Machine Learning and Wavelet Singular Values Approach" Electronics 10, no. 16: 1914. https://doi.org/10.3390/electronics10161914
APA StyleDehghani, M., Niknam, T., Ghiasi, M., Bayati, N., & Savaghebi, M. (2021). Cyber-Attack Detection in DC Microgrids Based on Deep Machine Learning and Wavelet Singular Values Approach. Electronics, 10(16), 1914. https://doi.org/10.3390/electronics10161914