FSCB-IDS: Feature Selection and Minority Class Balancing for Attacks Detection in VANETs
Abstract
:1. Introduction
2. Background and Related works
2.1. Background
2.2. Related Works
3. Proposed Methodology
3.1. Data Preprocessing
3.2. Feature Selection
3.3. Data Normalization and Classification
4. Results and Discussion
4.1. Environment Description
- TP: True positives define the amount of well identified intrusion.
- FP: False positives represent the amount of badly classified intrusion.
- TN: True negatives define the amount of correctly well normal occurrence.
- FN: False negatives represent the amount of misclassified normal occurrence.
4.2. Result Discussion
4.2.1. Using NSL-KDD Dataset
4.2.2. Using TON-IOT Dataset
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Author | Year | Used Learning Method | Accuracy (%) | Attacks Covered |
---|---|---|---|---|
Grover et al. [19] | 2011 | IBK Random Forest J−48 Naive Bayes AdaBoost1 | 56.0 92.0 42.0 92.0 92.0 | Multiple Misbehaviors |
Wahab et al. [16] | 2016 | SVM | Multiple Misbehaviors | |
Abdalla and Ahmed [14] | 2021 | Logistic regression Naive Bayes K-nearest Neighbor Decision tree XGBoost Support vector machine Random forest AdaBoost | 60.9 51.3 97.1 87.0 98.0 87.0 97.1 | Multiple Misbehaviors |
Al-Jarrah et al. [13] | 2014 | RF-FSR RF-BER | 99.90 99.88 | Multiple Misbehaviors |
H. Bangui et al. [17] | 2021 | Random Forest Bayesian-Coresets SVM CNN | 96.93 82.40 85.20 95.14 | Multiple Misbehaviors |
Khattab M et al. [40] | 2018 | SVM Neural network | 99.92 | Grey hole Rushing attack |
Omar A et al. [41] | 2016 | SVM | 99.67 | Multiple Misbehaviors |
D. Kosmanos et al. [42] | 2020 | RF KNN | 90 | Multiple Misbehaviors |
Predicted Normal | Predicted Attack | |
---|---|---|
Actual Normal | TN | FP |
Actual Attack | FN | TP |
Models | Accuracy | Precision | Recall | F1-Score |
---|---|---|---|---|
LR | 97.6 | 97.5 | 97.5 | 97.5 |
KNN | 99.8 | 99.8 | 99.8 | 99.8 |
DT | 99.8 | 99.8 | 99.8 | 99.8 |
SVM | 99.6 | 99.6 | 99.6 | 99.6 |
Proposed Model | 99.9 | 99.7 | 99.7 | 99.7 |
Classifier | Accuracy | Precision | Recall | F1-Score |
---|---|---|---|---|
LR | 73.9 | 75.1 | 74.1 | 73.9 |
KNN | 99.8 | 99.8 | 99.8 | 99.8 |
DT | 100 | 100 | 100 | 100 |
SVM | 50.0 | 24.9 | 49.9 | 66.6 |
Proposed Model | 100 | 100 | 100 | 100 |
Classifier | Accuracy | Precision | Recall | F1-Score |
---|---|---|---|---|
LR | 99.9 | 99.9 | 99.9 | 99.9 |
KNN | 99.9 | 99.9 | 99.9 | 99.9 |
DT | 99.9 | 99.9 | 99.9 | 99.9 |
SVM | 18.0 | 16.0 | 34.0 | 34.0 |
Proposed Model | 99.9 | 99.9 | 99.9 | 99.9 |
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Amaouche, S.; Guezzaz, A.; Benkirane, S.; Azrour, M.; Khattak, S.B.A.; Farman, H.; Nasralla, M.M. FSCB-IDS: Feature Selection and Minority Class Balancing for Attacks Detection in VANETs. Appl. Sci. 2023, 13, 7488. https://doi.org/10.3390/app13137488
Amaouche S, Guezzaz A, Benkirane S, Azrour M, Khattak SBA, Farman H, Nasralla MM. FSCB-IDS: Feature Selection and Minority Class Balancing for Attacks Detection in VANETs. Applied Sciences. 2023; 13(13):7488. https://doi.org/10.3390/app13137488
Chicago/Turabian StyleAmaouche, Sara, Azidine Guezzaz, Said Benkirane, Mourade Azrour, Sohaib Bin Altaf Khattak, Haleem Farman, and Moustafa M. Nasralla. 2023. "FSCB-IDS: Feature Selection and Minority Class Balancing for Attacks Detection in VANETs" Applied Sciences 13, no. 13: 7488. https://doi.org/10.3390/app13137488