Detection for Malicious Network Traffic Based on Convolutional Neural Networks

Yueyang Li

2023

Abstract

This research underscores the paramount significance of network security in our modern, interconnected digital landscape. It sheds light on the escalating threat posed by malicious network traffic, which poses risks to both sensitive information and critical infrastructure. Effectively detecting and mitigating these malicious flows is crucial for safeguarding our digital ecosystem. In response to this pressing concern, the study delves into the potential of deep learning, specifically Convolutional Neural Networks (CNNs), as a means to address the issue. It introduces a method for conducting multi-class classification on network traffic using deep learning techniques, leveraging the UNSW-NB15 dataset. The research highlights the remarkable superiority of the proposed 1D deep CNN architecture when compared to traditional methods. Moreover, this study looks ahead to the future, discussing the potential and challenges of implementing Artificial Intelligence (AI)-based detection systems in governmental and business settings. It underscores the necessity of collaboration and the adoption of explainable AI to ensure effective network security solutions in our rapidly evolving digital landscape.

Paper Citation

in Harvard Style

Li Y. (2023). Detection for Malicious Network Traffic Based on Convolutional Neural Networks. In Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-705-4, SciTePress, pages 321-325. DOI: 10.5220/0012805400003885

in Bibtex Style

@conference{daml23,
author={Yueyang Li},
title={Detection for Malicious Network Traffic Based on Convolutional Neural Networks},
booktitle={Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2023},
pages={321-325},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012805400003885},
isbn={978-989-758-705-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - Detection for Malicious Network Traffic Based on Convolutional Neural Networks
SN - 978-989-758-705-4
AU - Li Y.
PY - 2023
SP - 321
EP - 325
DO - 10.5220/0012805400003885
PB - SciTePress