On the Adoption of Explainable Deep Learning for Image-Based Network Traffic Classification

Amine Hattak, Amine Hattak, Fabio Martinelli, Francesco Mercaldo, Francesco Mercaldo, Antonella Santone

2024

Abstract

In an era marked by escalating cyber threats, ensuring the security of interconnected devices and networks within the Internet of Things (IoT) landscape is imperative. This paper addresses this pressing concern by delving into network security, focusing on the classification of network traffic through the lens of deep learning techniques. Our study presents a deep learning-based approach customized for network traffic classification in the IoT domain, based on image analysis. Crucially, to enhance the interpretability and the transparency in our model’s decisions, we integrate GradCAM (Gradient-weighted Class Activation Mapping), a technique that illuminates the salient regions of input images contributing to the model’s predictions. By leveraging GradCAM, we provide deeper insights into the decision-making process, enabling better understanding and trust in our approach. We evaluate the effectiveness of our methodology using the TON IoT dataset, consisting of 10 network traces categorized into various vulnerability scenarios and trusted applications. Our findings reveal a remarkable accuracy of 99.1%, demonstrating the potential of our approach in fortifying network security within IoT environments. Moreover, the utilization of GradCAM empowers stakeholders with valuable insights into the inner workings of the model, further enhancing its applicability and trustworthiness.

Paper Citation

in Harvard Style

Hattak A., Martinelli F., Mercaldo F. and Santone A. (2024). On the Adoption of Explainable Deep Learning for Image-Based Network Traffic Classification. In Proceedings of the 14th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH; ISBN 978-989-758-708-5, SciTePress, pages 370-377. DOI: 10.5220/0012802800003758

in Bibtex Style

@conference{simultech24,
author={Amine Hattak and Fabio Martinelli and Francesco Mercaldo and Antonella Santone},
title={On the Adoption of Explainable Deep Learning for Image-Based Network Traffic Classification},
booktitle={Proceedings of the 14th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH},
year={2024},
pages={370-377},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012802800003758},
isbn={978-989-758-708-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH
TI - On the Adoption of Explainable Deep Learning for Image-Based Network Traffic Classification
SN - 978-989-758-708-5
AU - Hattak A.
AU - Martinelli F.
AU - Mercaldo F.
AU - Santone A.
PY - 2024
SP - 370
EP - 377
DO - 10.5220/0012802800003758
PB - SciTePress