Network Intrusion Detection On Cicids2017
Métriques
Avg F1
Precision
Recall
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Avg F1 | Precision | Recall | Paper Title | Repository |
|---|---|---|---|---|---|
| DBN | 0.94 | 88.7 | 99.7 | An Intrusion Detection System based on Deep Belief Networks | |
| OC-SVM / RF | 0.9875 | 99.26 | 98.34 | A Novel Multi-Stage Approach for Hierarchical Intrusion Detection | |
| MLP | 0.873 | 81.7 | 99.5 | An Intrusion Detection System based on Deep Belief Networks | |
| Random Forest | 0.971 | 98.2 | 96.1 | Synthesis of a Machine Learning Model for Detecting Computer Attacks Based on the CICIDS2017 Dataset | |
| CNN-BiLSTM | 0.908 | 95.9 | 86.2 | Deep Learning Applications for Intrusion Detection in Network Traffic |
0 of 5 row(s) selected.