UC5 : Machine Learning vs DDoS
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
