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{{Commun | {{Commun | ||
|ShortDescription FR=Développement du Use Case pédagogique "Machine Learning vs Attaque DDoS" dans le cadre du GT IA et Cyber | |ShortDescription FR=Développement du Use Case pédagogique "Machine Learning vs Attaque DDoS" dans le cadre du GT IA et Cyber | ||
|Status=Production | |Status=Production | ||
}} | }} | ||
== 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 modeling. | ||
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) | * [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) | * [UNSW-NB15Datasets](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 an modeling notebooks. In some notebooks standardization step is include in the modeling notebook | ||
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 | |||
Clustering, Data Standardization | |||
==Plan of Study== | ==Plan of Study== | ||
* Data Analysis Notebook --\> Standardization Notebook --\> Modeling Notebook | |||
To make things easier each notebook can be run independently. | To make things easier each notebook can be run independently. | ||
Dataset Cse cic ids | == Dataset Cse cic ids == | ||
===Data=== | ===Data=== | ||
{| class="wikitable" | |||
| cleaned_ids2018S_test. | |+ | ||
|- | |cleaned_ids2018S_test.cs | ||
| cleaned_ids2018S_train.csv | | | | ||
| | |||
| | |||
|- | |||
|cleaned_ids2018S_train.csv | |||
| | |||
| | |||
| | |||
|} | |||
===Notebooks=== | ===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 == | |||
Dataset | |||
== | === 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 | |||
| | |||
| | |||
|} | |||
| Notebook | === 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/> | |||
{{PageSubHeader Commun | {{PageSubHeader Commun | ||
|WorkGroup=IA et cybersécurité | |WorkGroup=IA et cybersécurité | ||
}} | }} |
Dernière version du 13 novembre 2024 à 10:55
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
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[modifier | modifier le wikicode]
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[modifier | modifier le wikicode]
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[modifier | modifier le wikicode]
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 |
Notebooks du use case[modifier | modifier le wikicode]
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