An Intrusion Detection Method Based on Symmetric Federated Deep Learning in Complex Networks Article Swipe
Lei Wang
,
Xiaoquan Ren
,
Chun-Yi Wu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/sym17060952
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/sym17060952
The rapid development of the current 5G/6G network has added tremendous pressure to traditional security detection in the scenario of dealing with large-scale network attacks, resulting in high time complexity and low efficiency of attack identification. According to the deep network and its symmetry principle, this paper proposes a complex network intrusion detection and recognition method based on symmetric federation optimization, named IDS, which aims to reduce the time complexity and improve the accuracy and efficiency of attack identification. By using a symmetric network UNet-based deep feature learning to reconstruct data and construct the input matrix, we optimize the federated deep learning algorithm with a symmetric auto-encoder to make it more suitable for a complex network environment. The experimental results demonstrate that the technology based on the symmetric network proposed in this paper possesses significant advantages in terms of intrusion detection accuracy and effectiveness, which can effectively identify network intrusion and improve the accuracy of current complex network intrusion detection. The proposed symmetric intrusion detection method not only solves the bottleneck of traditional detection methods and improves the training efficiency of the model, but it also provides a new idea and solution for network security research.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/sym17060952
- OA Status
- gold
- Cited By
- 1
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411383093
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4411383093Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/sym17060952Digital Object Identifier
- Title
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An Intrusion Detection Method Based on Symmetric Federated Deep Learning in Complex NetworksWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-06-15Full publication date if available
- Authors
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Lei Wang, Xiaoquan Ren, Chun-Yi WuList of authors in order
- Landing page
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https://doi.org/10.3390/sym17060952Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/sym17060952Direct OA link when available
- Concepts
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Computer science, Intrusion detection system, Artificial intelligence, Data miningTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
- References (count)
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31Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |