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== Overview==
== Overview==
The use case focuses on vast varieties of intrusions and attack activities of
The use case focuses on vast varieties of intrusions and attack activities of
the network traffic. We propose three Dataset ready for exploration and
the network traffic. We propose three Dataset ready for exploration and modeling.
modeling.


[CSE-CIC-IDS2018
[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)
    Datasets](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-NB15
[UNSW-NB15Datasets](https://research.unsw.edu.au/projects/unsw-nb15-dataset)
    Datasets](https://research.unsw.edu.au/projects/unsw-nb15-dataset)


[USB-IDS Datasets](http://idsdata.ding.unisannio.it/index.html)
[USB-IDS Datasets](http://idsdata.ding.unisannio.it/index.html)


For each cyber security dataset we propose data analysis, data standardization
For each cyber security dataset we propose data analysis, data standardization

Version du 12 novembre 2024 à 13:58

Développement du Use Case pédagogique "Machine Learning vs Attaque DDoS" dans le cadre du GT IA et Cyber

Catégorie : Commun Statut : Production 1 : Idée - 2 : Prototype - 3 : Validation - 4 : Production


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Overview[modifier | modifier le wikicode]

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[modifier | modifier le wikicode]

- Data Analysis Notebook --\> Standardization Notebook --\> Modeling Notebook

To make things easier each notebook can be run independently.

Dataset Cse cic ids


Data[modifier | modifier le wikicode]

| cleaned_ids2018S_test.cs | |----------------------------| | cleaned_ids2018S_train.csv |

Notebooks[modifier | modifier le wikicode]

| 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[modifier | modifier le wikicode]

| 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[modifier | modifier le wikicode]

| 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[modifier | modifier le wikicode]

| USB-IDS-1S-TEST.csv | |---------------------------| | USB-IDS-1S-TRAIN.csv | | USB-IDS-1S-VALIDATION.csv |

Notebooks[modifier | modifier le wikicode]

| Notebook | Data Science step | |------------------------------|--------------------| | Cyber_USB-IDS_analysis.ipynb | *Data exploration* | | | |



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Groupe de travail

IA et cybersécurité