PulseLog: Context-Conditioned Behavioral Dynamics for Noise-Resilient Log Anomaly Detection

Log anomaly detection requires inferring system behavior from observations. Their semantic and contextual evidence can be distorted by template variation, parameter errors, formatting changes, and other observation noise. Although existing methods have advanced semantic representation learning and temporal dependency modeling, they largely treat observed events as homogeneous evidence. Persistent operational regimes, transient deviations, and runtime context are entangled in a single latent trajectory. This prevents the detector from separating observation-induced perturbations from behaviorally meaningful changes. It also makes anomaly decisions sensitive to benign fluctuations and contextual shifts. We propose PulseLog, a context-conditioned behavioral dynamics model for noise-resilient log anomaly detection. PulseLog maps log observations to semantic anchors. It calibrates runtime context. It adaptively routes persistent and transient evidence at multiple temporal rates. It evolves a latent behavioral state through context-gated dynamics. A deviation probe identifies departures from context-specific state-transition patterns. This enables window-level anomaly detection based on deviations in system-state dynamics. Experiments across five public log systems demonstrate strong detection performance under standard evaluation, observation-noise perturbations, limited training data, and cross-system adaptation. PulseLog also has a compact model footprint. The results indicate that explicitly modeling context-conditioned behavioral dynamics improves robustness under the evaluated log observation conditions.