Leveraging Deep Learning for Intrusion Detection in IoT Through Visualized Network Data

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

2024

Abstract

In an era marked by increasing reliance on digital technology, the security of interconnected devices and networks has become a paramount concern in the realm of information technology. Recognizing the pivotal role of network analysis in identifying and thwarting cyber threats, this paper delves into network security, specifically targeting the classification of network traffic using deep learning techniques within the Internet of Things (IoT) ecosystem. This paper introduces a deep learning-based approach tailored for traffic classification, beginning with raw traffic data in PCAP format. This data undergoes a transformation into visualized images, which serve as input for deep learning models designed to differentiate between benign and malicious activities. We evaluate the efficacy of our proposed method using the TON IoT dataset (Dr Nickolaos Koroniotis, 2021), comprising 10 network traces across two categories: nine related to diverse vulnerability scenarios and one associated with a trusted application. Our results showcase an impressive accuracy of 99.1%, underscoring the potential of our approach in bolstering network security within IoT environments.

Paper Citation

in Harvard Style

Hattak A., Martinelli F., Mercaldo F. and Santone A. (2024). Leveraging Deep Learning for Intrusion Detection in IoT Through Visualized Network Data. In Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT; ISBN 978-989-758-709-2, SciTePress, pages 722-729. DOI: 10.5220/0012768400003767

in Bibtex Style

@conference{secrypt24,
author={Amine Hattak and Fabio Martinelli and Francesco Mercaldo and Antonella Santone},
title={Leveraging Deep Learning for Intrusion Detection in IoT Through Visualized Network Data},
booktitle={Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT},
year={2024},
pages={722-729},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012768400003767},
isbn={978-989-758-709-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT
TI - Leveraging Deep Learning for Intrusion Detection in IoT Through Visualized Network Data
SN - 978-989-758-709-2
AU - Hattak A.
AU - Martinelli F.
AU - Mercaldo F.
AU - Santone A.
PY - 2024
SP - 722
EP - 729
DO - 10.5220/0012768400003767
PB - SciTePress