A Data-Driven Approach to Mitigate Evolving Volumetric Attacks in Programmable Networks
In-network machine learning (ML) offers a cutting-edge approach for promptly detecting malicious traffic. Existing methods often rely on one-size-fits-all ML models that fail to adapt to evolving attack traffic patterns, leading to a time-consuming and labor-intensive process for updating ML model f...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Transactions on Machine Learning in Communications and Networking |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11105473/ |
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