Improving detection of scanning attacks on heterogeneous networks with Federated Learning WAIN 2021
The source code for reproductibility of WAIN 2021 paper:
Improving detection of scanning attacks on heterogeneous networks with Federated Learning, see the paper presentation:
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 usingFedAvg.
Centralized.ipynb- Evaluation of naive scanning attack detection learning task centralizing all the data.
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
Use the pull request and issues tab on this repository to contribute.
@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},
}