ppopiolek/cyberaml

CyberAML

Prractial Adversarial Attacks on Flow-Based Network Intrusion Detection Systems

Create conda env for this project with:

conda create --name <env> --file requirements.txt

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources (e.g., processed ready-to-use network traffic flow datasets).
│   ├── interim        <- Intermediate data that has been transformed (e.g., raw pcaps after apllication of perturbation techniques).
│   ├── processed      <- The final, canonical data sets for modeling (e.g., fully perturbed network traffic flow datasets).
│   └── raw            <- The original, immutable data dump (e.g., raw pcaps).
│
├── models             <- Trained and serialized models, resulting perturbation steps, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
└── src                <- Source code for use in this project.
    ├─main.py        <- Project's main sccript
    │
    ├── data           <- Scripts to download or generate data
    │
    ├─operations     <- Scripts to turn raw pcaps into perturbed pcaps, or into ready network traffic flows
    │
    └── models         <- Scripts to train models and then use trained models to make predictions; generally to
                          find optimal perturbation steps

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Project based on the cookiecutter data science project template. #cookiecutterdatascience