Drift-Aware RL-based Wavelet Denoising for Network-Traffic Anomaly Detection
Traffic-utilisation measurements for network monitoring are corrupted by additive noise and statistical drift: time-dependent change in the signal's mean, variance, distributional shape, or tail behaviour. Static wavelet denoising, calibrated under stationary independent and identically distributed (i.i.d.) Gaussian assumptions, becomes mismatched under drift and, at moderate-to-high signal-to-noise ratio (SNR), over-suppresses useful structure and degrades monitoring decisions. We propose a drift-aware framework treating adaptive wavelet denoising as a preprocessing layer optimised for two tasks: anomaly detection, recovering the multi-scale transient load bursts that noise and drift obs...
| Authors | Priyalakshmi Sheela, Indrakshi Dey |
|---|---|
| Primary category | eess.SP |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
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