A Two-Phase Feature Selection Framework for Intrusion Detection System: Balancing Relevance and Computational Efficiency (2P-FSID)
The rapid growth of data demands robust security mechanisms to prevent unauthorized access, making ML-based intrusion detection systems essential. However, high-dimensional data necessitates the need for effective feature selection. This study proposes the Two-Phase Feature Selection framework for I...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2025.2539396 |
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