Improving Transferability of Network Intrusion Detection in a Federated Learning Setup

Shreya Ghosh, Abu Shafin Mohammad Mahdee Jameel, Aly El Gamal
1/7/2024
cs.CRcs.LGeess.SP

Official code: ghosh64/transferability (13 stars, last commit 2023-12-23).

Abstract

Network Intrusion Detection Systems (IDS) aim to detect the presence of an intruder by analyzing network packets arriving at an internet connected device. Data-driven deep learning systems, popular due to their superior performance compared to traditional IDS, depend on availability of high quality training data for diverse intrusion classes. A way to overcome this limitation is through transferable learning, where training for one intrusion class can lead to detection of unseen intrusion classes after deployment. In this paper, we provide a detailed study on the transferability of intrusion detection. We investigate practical federated learning configurations to enhance the transferability of intrusion detection. We propose two techniques to significantly improve the transferability of a federated intrusion detection system. The code for this work can be found at https://github.com/ghosh64/transferability.

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Improving Transferability of Network Intrusion Detection in a Federated Learning Setup

131PythonDec 22, 20232 years ago

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Cite this paper

@article{ghosh2024improving,
  title  = {Improving Transferability of Network Intrusion Detection in a Federated Learning Setup},
  author = {Shreya Ghosh and Abu Shafin Mohammad Mahdee Jameel and Aly El Gamal},
  year   = {2024},
  eprint = {2401.03560},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.03560v1}
}

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