Stacked Ensemble Deep Learning for Robust Intrusion Detection in IoT Networks

Marwa Amara, Marwa Amara, Nadia Smairi, Mohamed Jaballah

2025

Abstract

Intrusion Detection Systems (IDS) are critical for addressing the growing complexity of cyber threats in the Internet of Things (IoT) domain. This paper introduces a novel stacked ensemble approach combining Convolutional Neural Networks (CNN), Temporal Convolutional Networks (TCN), and Long Short-Term Memory (LSTM) models through a logistic regression meta-model. The proposed approach leverages the distinct strengths of each classifier; sequential pattern recognition by LSTMs, temporal dependency modeling by TCNs, and spatial feature extraction by CNNs to create a robust and reliable detection framework. To address the class imbalance problem, we applied various balancing techniques, including Oversampling, Undersam-pling, and a hybrid Meet-in-the-Middle method. The effectiveness of the approach is demonstrated on the CICIDS2017 dataset, achieving an accuracy of 99.99% and an F1-score of 100% with Oversampling, and 99.93% accuracy with the Meet-in-the-Middle technique.

Paper Citation

in Harvard Style

Amara M., Smairi N. and Jaballah M. (2025). Stacked Ensemble Deep Learning for Robust Intrusion Detection in IoT Networks. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 1146-1153. DOI: 10.5220/0013290700003890

in Bibtex Style

@conference{icaart25,
author={Marwa Amara and Nadia Smairi and Mohamed Jaballah},
title={Stacked Ensemble Deep Learning for Robust Intrusion Detection in IoT Networks},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={1146-1153},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013290700003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Stacked Ensemble Deep Learning for Robust Intrusion Detection in IoT Networks
SN - 978-989-758-737-5
AU - Amara M.
AU - Smairi N.
AU - Jaballah M.
PY - 2025
SP - 1146
EP - 1153
DO - 10.5220/0013290700003890
PB - SciTePress