SCAST: Spectral Constraint-Aware Selective Transfer for Cross-System Log Anomaly Detection

Cross-system log anomaly detection transfers knowledge across systems with different event vocabularies and interaction structures, often under limited target-domain supervision. A key challenge is deciding which structural dependencies should be transferred before reducing source–target discrepancy. We present SCAST, a spectral constraint-aware selective transfer framework that performs dependency selection before alignment. SCAST maps heterogeneous log graphs into a shared semantic–structural space and decomposes their structural signals into multiscale spectral bands. Within each band, temporal direction and structural priors guide the modeling of dependency candidates, while a learned router separates features used for transfer from residual information. The selected band-wise representations are then fused into sample-level summaries for cross-domain alignment and anomaly scoring, rather than aligning an entangled whole-graph representation. We analyze the effects of cross-band fusion and the trade-off between reducing domain discrepancy and retaining predictive information.Experiments on four public log datasets show that SCAST performs competitively in cross-system transfer while remaining effective in single-system detection. Component and alignment-placement comparisons indicate that screening dependencies before alignment is more effective than aligning mixed representations. In an extended diagnostic model, frequency-resolved perturbations produce larger mean anomaly-logit changes for the screened branch than for residual and random perturbations in each of the three tested structural bands.