Anomaly Detection in IoT Networks: A Performance Comparison of Transformer, 1D-CNN, and GrowNet Models on the Bot-IoT Dataset

Aurelia Kusumastuti, Denis Rangelov, Philipp Lämmel, Michell Boerger, Andrei Aleksandrov, Nikolay Tcholtchev, Nikolay Tcholtchev

2025

Abstract

This paper presents an exploratory analysis of deep learning techniques for intrusion detection in IoT networks. Specifically, we investigate three innovative intrusion detection systems based on transformer, 1D-CNN and GrowNet architectures, comparing their performance against random forest and three-layer perceptron models as baselines. For each model, we study the multiclass classification performance using the publicly available IoT network traffic dataset Bot-IoT. We use the most important performance indicators, namely, accuracy, F1-score, and ROC, but also training and inference time to gauge the utility and efficacy of the models. In contrast to earlier studies where random forests were the dominant method for ML-based intrusion detection, our findings indicate that the transformer architecture outperforms all other methods in our approach.

Paper Citation

in Harvard Style

Kusumastuti A., Rangelov D., Lämmel P., Boerger M., Aleksandrov A. and Tcholtchev N. (2025). Anomaly Detection in IoT Networks: A Performance Comparison of Transformer, 1D-CNN, and GrowNet Models on the Bot-IoT Dataset. In Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-758-0, SciTePress, pages 633-643. DOI: 10.5220/0013637600003967

in Bibtex Style

@conference{data25,
author={Aurelia Kusumastuti and Denis Rangelov and Philipp Lämmel and Michell Boerger and Andrei Aleksandrov and Nikolay Tcholtchev},
title={Anomaly Detection in IoT Networks: A Performance Comparison of Transformer, 1D-CNN, and GrowNet Models on the Bot-IoT Dataset},
booktitle={Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2025},
pages={633-643},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013637600003967},
isbn={978-989-758-758-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Anomaly Detection in IoT Networks: A Performance Comparison of Transformer, 1D-CNN, and GrowNet Models on the Bot-IoT Dataset
SN - 978-989-758-758-0
AU - Kusumastuti A.
AU - Rangelov D.
AU - Lämmel P.
AU - Boerger M.
AU - Aleksandrov A.
AU - Tcholtchev N.
PY - 2025
SP - 633
EP - 643
DO - 10.5220/0013637600003967
PB - SciTePress