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Network intrusion models can continuously absorb emerging zero-day attack patterns without catastrophic forgetting by pairing latent manifold drift tracking with selective parameter freezing.
Oscillatory-state alignment in AOSNET significantly enhances forecasting accuracy in non-stationary time series, outperforming traditional fixed-template methods.
