Translations:UC5 : Machine Learning vs DDoS/15/en

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Version datée du 2 janvier 2025 à 14:52 par Juliette (discussion | contributions) (Page créée avec « === Notebooks === {| class="wikitable" |+ !Notebook !Data Science step      ! |- |Cyber_unsw_analysis.ipynb |''Data exploration'' | |- |Cyber_unsw_analysisGmm.ipynb |''data exploration for GMM clustering'' | |- |Cyber_unsw_standardization.ipynb |''data standardization'' | |- |Cyber_unsw_autoencoder.ipynb   |''Binary classifier study. Half-Supervised Autoencoder modeling, we tested:'' | |- | | -logistic regression    | |- | | -Autoencoder Inria like   ... »)
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Notebooks

Notebook Data Science step     
Cyber_unsw_analysis.ipynb Data exploration
Cyber_unsw_analysisGmm.ipynb data exploration for GMM clustering
Cyber_unsw_standardization.ipynb data standardization
Cyber_unsw_autoencoder.ipynb   Binary classifier study. Half-Supervised Autoencoder modeling, we tested:
-logistic regression   
-Autoencoder Inria like   
-Autoencoder single layer   
-Autoencoder multi layers   
Cyber_unsw_complete_analysis.ipynb   data exploration
Cyber_unsw_model.ipynb   Data Supervised model, to classify attacks of different kinds, we tested:
-Random Forest Classifier (rfc)
-Support Vector Classification (svm)
-Multi-Layer Perceptron (mlp)
-Artificial Neural Network (ann)
-eXtreme Gradient Boosting (xgb)
-Convolutional Neural Network (cnn)