Improving detection of scanning attacks on heterogeneous networks with Federated Learning WAIN 2021

DOI: 10.1145/3543146.3543172

The source code for reproductibility of WAIN 2021 paper:

Improving detection of scanning attacks on heterogeneous networks with Federated Learning, see the paper presentation:

Description of the notebooks

Federated Learning.ipynb - All data wrangling for the federated learning experiment reported on the paper. With 13 agents (data silos) comparing local trained models to federated learning using FedAvg.

Centralized.ipynb - Evaluation of naive scanning attack detection learning task centralizing all the data.

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How to run

All dependecies for running these notebooks are available on the requirements.txt file. Using python with virtualenv to setup the environment on Linux and access the notebooks:

$ python -m venv venv
$ source venv\bin\activate
(venv) $ pip install -r requirements.txt
(venv) $ jupyter notebook

How to contribute

Use the pull request and issues tab on this repository to contribute.

Citing

@article{10.1145/3543146.3543172,
  author = {de Carvalho Bertoli, Gustavo and Pereira J\'{u}nior, Louren\c{c}o Alves and Saotome, Osamu},
  title = {Improving Detection of Scanning Attacks on Heterogeneous Networks with Federated Learning},
  year = {2022},
  issue_date = {March 2022},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  volume = {49},
  number = {4},
  issn = {0163-5999},
  url = {https://doi.org/10.1145/3543146.3543172},
  doi = {10.1145/3543146.3543172},
  journal = {SIGMETRICS Perform. Eval. Rev.},
  month = {jun},
  pages = {118–123},
  numpages = {6},
}