Modifier Commun : UC5 : Machine Learning vs DDoS/fr

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== Overview== The use case focuses on vast varieties of intrusions and attack activities of the network traffic. We propose three Dataset ready for exploration and modeling. * [CSE-CIC-IDS2018Datasets](https://www.unb.ca/cic/datasets/ids-2018.html#:~:text=In%20CSE-CIC-IDS2018%20dataset%2C%20we%20use%20the%20notion%20of,human%20operators%20to%20generate%20events%20on%20the%20network) * [UNSW-NB15Datasets](https://research.unsw.edu.au/projects/unsw-nb15-dataset) * [USB-IDS Datasets](http://idsdata.ding.unisannio.it/index.html) For each cyber security dataset we propose data analysis, data standardization an modeling notebooks. In some notebooks standardization step is include in the modeling notebook * Authors: Christian Maréchal * Keywords: Supervised Machine Learning, Half-Supervised Machine Learning, Clustering, Data Standardization ==Plan of Study== * Data Analysis Notebook --\> Standardization Notebook --\> Modeling Notebook To make things easier each notebook can be run independently. == Dataset Cse cic ids == ===Data=== {| class="wikitable" |+ |cleaned_ids2018S_test.cs      | | | |- |cleaned_ids2018S_train.csv | | | |} ===Notebooks=== {| class="wikitable" |+ !Notebook !Data Science step ! |- |Cyber_cse-cic-ids_analysis.ipynb |''Data exploration'' | |- |Cyber_cse-cic-ids_model.ipynb   |''Standardization and Half-Supervised Autoencoder model'' | |} == Dataset Unsw == === Data === {| class="wikitable" |+ ! ! ! |- |NUSW-NB15_features.utf8.csv      | | |- |UNSW_NB15S_test.csv    | | |- |UNSW_NB15S_train.csv | | |- |UNSW-NB15_1.csv | | |- |UNSW-NB15_2.csv | | |- |UNSW-NB15_3.csv | | |- |UNSW-NB15_4.csv | | |} === 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    | |- | | -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) | |} == Dataset USB IDS == === Data === {| class="wikitable" |+ ! ! ! |- |USB-IDS-1S-TEST.csv      | | |- |USB-IDS-1S-TRAIN.csv    | | |- |USB-IDS-1S-VALIDATION.csv | | |} === Notebooks === {| class="wikitable" |+ !Notebook !Data Science step      ! |- |Cyber_USB-IDS_analysis.ipynb |''Data exploration'' | |} == Notebooks du use case == Retrouvez tous les éléments du Use Case sur le GitLab du Campus Cyber :https://gitlab.com/campuscyber/gt-ia-et-cyber/-/tree/main/UC5%20Cyber%20Attack%20Use%20Case%20-%20Machine%20Learning%20Vs%20DDoS?ref_type=heads<nowiki/>

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