On a 57-day, ten-router Internet2 traffic corpus, dense MLP forecaster TiDE cuts baseline prediction error by 30 to 42 percent relative to SARIMA and XGBoost, while anomaly masking gives only small robustness gains.
Ai-driven multilayered cybersecurity intelli- gence framework for critical infrastructure protection,
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Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining
On a 57-day, ten-router Internet2 traffic corpus, dense MLP forecaster TiDE cuts baseline prediction error by 30 to 42 percent relative to SARIMA and XGBoost, while anomaly masking gives only small robustness gains.