Research on Industrial Internet Intrusion Detection Based on Deep Learning Algorithms

Chengbo Jia

doi:10.54254/2755-2721/2025.20597

Research Article

Open access

Published on 24 January 2025

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Jia,C. (2025). Research on Industrial Internet Intrusion Detection Based on Deep Learning Algorithms. Applied and Computational Engineering,133,33-37.

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Research on Industrial Internet Intrusion Detection Based on Deep Learning Algorithms

Chengbo Jia *^,1,

¹ Hebei University of Technology, Lang Fang, China, 065000

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/2025.20597

Abstract

With the rapid popularization of the Industrial Internet, the connection between industrial control systems and enterprise networks has become increasingly tight, making them primary targets for cyberattacks. This trend not only intensifies the security risks of industrial control systems but also presents new challenges for cybersecurity protection. Deep learning technology in artificial intelligence, with its capability to learn complex problems from unsupervised data, is a powerful tool for protecting the cybersecurity of industrial control systems. This paper explores the foundational theories of Industrial Internet security and intrusion detection, covering basic concepts, major characteristics, and various security threats and risks faced by the Industrial Internet. It further analyzes the limitations of traditional security technologies in the context of the Industrial Internet, including their inadequacies in responding to new threats, particularly their vulnerability to system vulnerabilities and virus attacks. Moreover, the paper introduces the specific applications of three deep learning algorithms—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs)—in the security of the Industrial Internet. Through systematic analysis and research, the paper reveals specific cybersecurity issues present in the Industrial Internet and proposes effective methods to address these issues using deep learning algorithms. These findings not only provide new ideas and technical support for contemporary Industrial Internet security protection but also offer valuable insights and a theoretical foundation for future research directions.

Keywords

Deep Learning Algorithms, Convolutional Neural Network (CNN), Machine Learning, Industrial Internet Intrusion

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References

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Cite this article

Jia,C. (2025). Research on Industrial Internet Intrusion Detection Based on Deep Learning Algorithms. Applied and Computational Engineering,133,33-37.

Data availability

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

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About volume

Volume title: Proceedings of the 5th International Conference on Signal Processing and Machine Learning

Conference website: https://2025.confspml.org/

ISBN：978-1-83558-943-4(Print) / 978-1-83558-944-1(Online)

Conference date: 12 January 2025

Editor：Stavros Shiaeles

Series: Applied and Computational Engineering

Volume number: Vol.133

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

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