基于漂移感知强化学习的小波去噪用于网络流量异常检测
Drift-Aware RL-based Wavelet Denoising for Network-Traffic Anomaly Detection
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中文总结 AI 辅助
研究针对网络流量监测受噪声和漂移影响问题，提出基于漂移感知强化学习的小波去噪框架，通过特定判断和选择机制优化异常检测及容量估计，以任务效用为奖励并与多种方法对比基准测试。
中文摘要 AI 辅助
网络监测中的流量利用率测量会受到加性噪声和统计漂移的影响，静态小波去噪在漂移情况下会不匹配。本文提出一个漂移感知框架，将自适应小波去噪作为预处理层，针对异常检测和容量估计进行优化。通过四探测器门判断何时调用学习策略，近端策略优化智能体在混合离散-连续动作空间中选择小波配置。奖励基于下游任务效用。与低通移动平均滤波器等进行对比基准测试，明确异常目标和漂移门以保证过程非循环。
英文摘要
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 obscure, and capacity estimation, recovering the operational required capacity $C_{95}$ (95th percentile of utilisation). Because localised bursts are multi-scale structure a wavelet preserves but a low-pass filter removes, detection discriminates denoiser families. A four-detector gate (Page-Hinkley, variance-ratio, Jensen-Shannon, Anderson-Darling) determines when to invoke a learned policy, and a Proximal Policy Optimization agent selects a per-window wavelet configuration over a mixed discrete-continuous action space. Unlike prior work, the reward is downstream task utility, not reconstruction fidelity. The denoiser is benchmarked, per drift type and input SNR, against a low-pass moving-average filter, VisuShrink, SureShrink, BayesShrink, and a Wiener filter. Defining the anomaly target on the clean signal and the drift gate on the corruption keeps both stages non-circular.
发表机构
- Walton Institute, Department of Computing, South East Technological University（沃顿研究所，计算系，东南技术大学）
机构由 AI 辅助整理，请以论文原文为准。
