A Proficient ZESO-DRKFC Model for Smart Grid SCADA Security
Abstract
:1. Introduction
1.1. Motivation
1.2. Problem Statement
1.3. Objectives
- Data preprocessing is carried out to normalize the original SCADA benchmarking datasets, which includes the operations of content characterization, scalar calculation, and category transformation.
- An innovative Zaire Ebola Search Optimization (ZESO) method that offers the best option for producing the optimized feature set is used to extract the most pertinent features from the normalized dataset.
- A sophisticated Deep Random Kernel Forest Classification (DRKFC) mechanism is used to predict the normal and attack data flows from the SCADA dataset.
- Some of the well-known SCADA benchmarking datasets are used to evaluate the effectiveness and outcomes of the proposed ZESO-RKFC security framework.
1.4. Organization
2. Literature Review
- Risk management;
- Malware protection;
- Vulnerability assessment;
- Security control;
- Cyber-risk analysis;
- Physical security.
Research Gap
- High delay;
- Overfitting;
- Decreased detection accuracy;
- Ineffective handling of massive datasets;
- Complexity of computation.
3. Materials and Methods
- SCADA benchmark dataset obtainment;
- Normalization;
- ZESO-based feature selection;
- DRKFC-based attack prediction;
- Performance evaluation.
3.1. Preprocessing
3.2. Zaire Ebola Search Optimization (ZESO)
- The newly infected persons (nP) are generated based on the set (s);
- Then, the generated persons are added in αc;
- According to the size of , the number of individuals is estimated and added to , , , , and .
- Consequently, update ,) based on ;
- Choose the current best value from , and compare it with the global best;
- Finally, return the global best solution with the optimal solution;
3.3. Deep Random Kernel Forest Classification (DRKFC)
4. Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Parameters | Descriptions |
---|---|
Transformed input parameter | |
Specified range of input variables | |
Initial range of input parameters (minimum and maximum values of the dataset) | |
Infected individual | |
Exposed individual | |
Susceptible | |
Recovered | |
Dead | |
Vaccinated | |
Hospitalized | |
Quarantined | |
Agents can infect people | |
Contact rate of infection | |
Contact rate of pathogen | |
Contact rate of deceased | |
Contact rate of recovered | |
Natural death rate | |
Disease-induced death rate | |
Recruitment rate | |
Rate response to vaccination | |
Z | Recovery rate |
S | Rate of burial of deceased individual |
V | Rate of vaccination of individual |
Rate of quarantine | |
K | Number of trees |
Number of leaves | |
M | Matrix |
P | Regularization parameter |
Loss function |
Datasets | Name |
---|---|
Dataset 1 | NSL-KDD |
Dataset 2 | CAN intrusion |
Dataset 3 | CIC-IDS 2017 |
Dataset 4 | BoT-IoT |
Dataset 5 | DS2OS |
Dataset 6 | UNSW-NB 15 |
Dataset 7 | CIRA-CIC-DoHBrw-2020 |
Methods | Precision | Recall | F1-Measure | Detection rate | FAR |
---|---|---|---|---|---|
LR | 78.1 | 80.1 | 79.1 | 80 | 11.50 |
XGB | 84.5 | 83.4 | 83.9 | 83 | 9.13 |
DT | 87.3 | 88.5 | 87.9 | 88 | 7.8 |
HCNN | 96.3 | 97.12 | 97.6 | 97 | 2.5 |
Proposed | 99.2 | 98.9 | 99.1 | 99.2 | 1.5 |
Methods | Accuracy |
---|---|
DBN | 95 |
DNN | 90.25 |
DL | 95 |
LSTM | 96.2 |
IDS-DL | 96 |
CNN IDS | 96 |
HCRNN | 97.75 |
Proposed | 99.3 |
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Rabie, O.B.J.; Balachandran, P.K.; Khojah, M.; Selvarajan, S. A Proficient ZESO-DRKFC Model for Smart Grid SCADA Security. Electronics 2022, 11, 4144. https://doi.org/10.3390/electronics11244144
Rabie OBJ, Balachandran PK, Khojah M, Selvarajan S. A Proficient ZESO-DRKFC Model for Smart Grid SCADA Security. Electronics. 2022; 11(24):4144. https://doi.org/10.3390/electronics11244144
Chicago/Turabian StyleRabie, Osama Bassam J., Praveen Kumar Balachandran, Mohammed Khojah, and Shitharth Selvarajan. 2022. "A Proficient ZESO-DRKFC Model for Smart Grid SCADA Security" Electronics 11, no. 24: 4144. https://doi.org/10.3390/electronics11244144
APA StyleRabie, O. B. J., Balachandran, P. K., Khojah, M., & Selvarajan, S. (2022). A Proficient ZESO-DRKFC Model for Smart Grid SCADA Security. Electronics, 11(24), 4144. https://doi.org/10.3390/electronics11244144