Voir la source de UC5 : Machine Learning vs DDoS

== Dataset Unsw ==

=== Data ===
{| class="wikitable"
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|NUSW-NB15_features.utf8.csv     
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|UNSW_NB15S_test.csv   
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|UNSW_NB15S_train.csv
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|UNSW-NB15_1.csv
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|UNSW-NB15_2.csv
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|UNSW-NB15_3.csv
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|UNSW-NB15_4.csv
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=== Notebooks ===
{| class="wikitable"
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!Notebook
!Data Science step     
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|Cyber_unsw_analysis.ipynb 
|''Data exploration''
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|Cyber_unsw_analysisGmm.ipynb 
|''data exploration for GMM clustering''
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|Cyber_unsw_standardization.ipynb 
|''data standardization''
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|Cyber_unsw_autoencoder.ipynb  
|''Binary classifier study. Half-Supervised Autoencoder modeling, we tested:''
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| -logistic regression   
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| -Autoencoder Inria like   
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| -Autoencoder single layer   
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| -Autoencoder multi layers   
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|Cyber_unsw_complete_analysis.ipynb  
|''data exploration''
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|Cyber_unsw_model.ipynb  
|''Data Supervised model, to classify attacks of different kinds, we tested:''
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| -Random Forest Classifier (rfc)
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| -Support Vector Classification (svm)
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| -Multi-Layer Perceptron (mlp)
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| -Artificial Neural Network (ann)
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| -eXtreme Gradient Boosting (xgb)
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| -Convolutional Neural Network (cnn)
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|}