Intrusion Detection on Intrusion Detection in Internet of Vehicles dataset
99.81AccuracyDecision Tree
Evaluation results
| Method | Links | ||
|---|---|---|---|
| Decision TreeFeature Set=ANOVA2025.12 | 99.81 | 75.99 | |
| Random ForestFeature Set=ANOVA2025.12 | 99.81 | 82.66 | |
| Random ForestFeature Set=Original2025.12 | 99.81 | 75.98 | |
| Manual Hard VotingFeature Set=Original Ensemble2025.12 | 99.81 | 75.98 | |
| Manual Soft VotingFeature Set=Original Ensemble2025.12 | 99.81 | 75.98 | |
| Final Hybrid EnsembleFeature Set=Original Ensemble2025.12 | 99.81 | 75.98 | |
| Decision TreeFeature Set=Original2025.12 | 99.72 | 79.31 | |
| Random ForestFeature Set=LDA2025.12 | 99.63 | 49.96 | |
| Random ForestFeature Set=PCA2025.12 | 99.63 | 35.96 | |
| DNN MLPFeature Set=Original2025.12 | 99.63 | 61.31 | |
| 1D CNNFeature Set=Original2025.12 | 99.53 | 29.95 | |
| Logistic RegressionFeature Set=ANOVA2025.12 | 99.44 | 19.94 | |
| Logistic RegressionFeature Set=LDA2025.12 | 99.44 | 19.94 | |
| Logistic RegressionFeature Set=PCA2025.12 | 99.44 | 19.94 | |
| Logistic RegressionFeature Set=Original2025.12 | 99.44 | 19.94 | |
| Decision TreeFeature Set=LDA2025.12 | 99.07 | 63.54 | |
| Decision TreeFeature Set=PCA2025.12 | 98.88 | 51.32 |