TL;DR
Deployment Oriented Dual Path Multi View Anomaly Detection (DP-MVAD), a deployment-efficient framework designed for complex non-stationary conditions, achieves trend-perturbation disentanglement through a conditionally identifiable dual-path approximation and ensures robust cross-scenario representations.
Time series anomaly detection faces a critical challenge that different anomaly types require different detection mechanisms, yet single methods are inherently limited by their design biases. We propose FlowFuse, a multi-view ensemble framework with coupling flow-based score fusion for time series anomaly detection. Fl...
Networked systems continuously generate heterogeneous time series, including Key Performance Indicator (KPI) streams, logs, and spectrum measurements, whose interpretation is essential for automated monitoring, diagnosis, and control. Existing analysis approaches either rely heavily on labeled data specific to each dep...
This work formalizes the problem, identifies five intrinsic characteristics underlying its core challenges, and organizes prior work into two complementary paradigms, which sharpens decision boundaries through coarse-grained, structural, and semantic anomaly injection.
The rapid development of the Internet of Vehicles (IoV) has made cyberattacks targeting vehicular time-series data a critical security issue. Existing reconstruction-based detection methods mainly rely on global sequence modeling, which limits their effectiveness in highly dynamic vehicular environments and often resul...
Robust anomaly detection in time series remains challenging because sparse abnormal observations, noise contamination, nonlinear dynamics, and long-range temporal dependencies can obscure deviation patterns. This paper proposes the SALK anomaly detection model, which integrates an attention-enhanced long short-term mem...
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