Research Article
Web Application Firewall Using Machine Learning and Features Engineering
Table 5
Classification accuracy of our proposed model for various datasets using Naive Bayes.
| | | CSIC 2010 | HTTPParams 2015 | Hybrid dataset | Compromised web server dataset |
| | Number of normal requests | 28,800 | 19,305 | 48,105 | 60250 | | Number of anomaly | 11,213 | 11,764 | 22,977 | 5210 | | Classification accuracy (80% training, 20% testing) | 99.59% | 97.91% | 96.40% | 98.80% | | Classification accuracy (100-fold cross-validation) | 99.71% | 98.02% | 96.66% | 98.97% | | False positive rate | 0.54% | 1.20% | 3.35% | 0.84% |
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