Voir la source de UC5 : Machine Learning vs DDoS

{{Commun
|ShortDescription FR=Développement du Use Case pédagogique "Machine Learning vs Attaque DDoS" dans le cadre du GT IA et Cyber
|Status=Production
}}
== 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/>
{{PageSubHeader Commun
|WorkGroup=IA et cybersécurité
}}