AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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RL-FAT is proposed, a reinforcement-learning-inspired fair adversarial training framework that uses policy-gradient based feedback from adversarial predictions to improve adversarial robustness while promoting a more balanced robustness distribution across classes.
The original single-step Decision score used by TCCM is sensitive to contamination, whereas trajectory-based Deviation and Reconstruction scores provide more stable anomaly signals, and Forest-Flow becomes competitive with, and in some cases outperforms, TCCM.
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