Optimizing Federated Learning for Intrusion Detection in IoT Networks

Abderahmane Hamdouchi, Ali Idri

2024

Abstract

The Internet of Things (IoT) involves billions of interconnected devices, making IoT networks vulnerable to cyber threats. To enhance security, deep learning (DL) techniques are increasingly used in intrusion detection systems (IDS). However, centralized DL-based IDSs raise privacy concerns, prompting interest in Federated Learning (FL). This research evaluates FL configurations using dense neural networks (DNN) and convolutional neural networks (CNN) with two optimizers, stochastic gradient descent (SGD) and Adam, across 20% and 60% feature thresholds. Two cost-sensitive learning techniques were applied: undersampling with binary cross-entropy and weighted classes using weighted binary cross-entropy. Using the NF-ToN-IoT-v2 dataset, 16 FL configurations were analyzed. Results indicate that SGD, combined with CNN and the Undersampling technique applied to the top 7 features, outperformed other configurations.

Paper Citation

in Harvard Style

Hamdouchi A. and Idri A. (2024). Optimizing Federated Learning for Intrusion Detection in IoT Networks. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN 978-989-758-716-0, SciTePress, pages 448-456. DOI: 10.5220/0013040000003838

in Bibtex Style

@conference{kdir24,
author={Abderahmane Hamdouchi and Ali Idri},
title={Optimizing Federated Learning for Intrusion Detection in IoT Networks},
booktitle={Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2024},
pages={448-456},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013040000003838},
isbn={978-989-758-716-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Optimizing Federated Learning for Intrusion Detection in IoT Networks
SN - 978-989-758-716-0
AU - Hamdouchi A.
AU - Idri A.
PY - 2024
SP - 448
EP - 456
DO - 10.5220/0013040000003838
PB - SciTePress