Research on Network Security Strategies Based on Deep Learning

Zhiyi Wu

doi:10.54254/2755-2721/2025.LD28455

Applied and Computational EngineeringOpen access

Research on Network Security Strategies Based on Deep Learning

Research Article

Open Access

Research on Network Security Strategies Based on Deep Learning

Zhiyi Wu ^1*

¹ Suzhou Foreign Language School, Suzhou, China, 215000

^*Corresponding author: wuzhiyi34@gmail.com

Published on 22 October 2025

ACE Vol.196

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-451-9

ISBN (Online): 978-1-80590-452-6

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Abstract

Deep learning techniques have gained significant traction in various domains, particularly in network security. This article discusses the fundamental principles of deep learning, including neural networks and important models like the Feed-Forward Neural Network (FNN), the Convolutional Neural Network (CNN), the Recurrent Neural Network (RNN), and Autodesk. Each model's unique architecture and functionality are discussed, with a focus on their applications in intrusion detection and network stream optimization. The challenges faced by deep learning in network security, such as increased model complexity and resource demands, are also examined. Finally, future trends indicate a push towards more lightweight models to enhance security in an increasingly interconnected digital landscape.

Keywords:

Deep Learning, Network Security, Intrusion Detection

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References

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References

[1]. Patterson, J., & Gibson, A. (2017). Deep learning: A practitioner's approach. " O'Reilly Media, Inc.".

[2]. Kotsiopoulos T, Sarigiannidis P, Ioannidis D, Tzovaras D (2021). Machine learning and deep learning in smart manufacturing: the innovative grid paradigm. Comput Sci Rev 40: 100341

[3]. Guresen, E., & Kayakutlu, G. (2011). Definition of artificial neural networks with comparison to other networks. Procedia Computer Science, 3, 426-433.

[5]. Pascanu, R., Gulcehre, C., Cho, K., & Bengio, Y. (2013). How to construct deep recurrent neural networks. arXiv preprint arXiv: 1312.6026.

[7]. Kasongo, S. M., & Sun, Y. (2019). A deep learning method with filter-based feature engineering for a wireless intrusion detection system. IEEE Access, 7, 38597-38607.

Cite this article

Wu,Z. (2025). Research on Network Security Strategies Based on Deep Learning. Applied and Computational Engineering,196,68-73.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

ISBN: 978-1-80590-451-9(Print) / 978-1-80590-452-6(Online)

Editor: Hisham AbouGrad

Conference website: https://www.confmla.org/london.html

Conference date: 12 November 2025

Series: Applied and Computational Engineering

Volume number: Vol.196

ISSN: 2755-2721(Print) / 2755-273X(Online)

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