A network intrusion detection method based on contrastive learning and Bayesian Gaussian Mixture Model
Abstract Network Intrusion Detection Systems (NIDS) are essential for safeguarding networks against malicious activities. However, existing machine learning-based NIDS often require complex feature engineering, which demands significant domain expertise and experimentation, leading to suboptimal mod...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-06-01
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| Series: | Cybersecurity |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s42400-025-00364-7 |
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