Anomaly-Aware Graph-Based Semi-Supervised Deep Support Vector Data Description for Multi-Channel Time-Series Anomaly Detection with Application to Fire Monitoring

Anomaly detection in safety-critical systems often operates under severe label constraints where only a small subset of normal and anomalous samples can be reliably annotated, while large unlabeled data streams are contaminated and high-dimensional. Deep one-class methods, such as deep support vector data description (DeepSVDD) and deep semi-supervised anomaly detection (DeepSAD), address this setting. However, they treat samples largely in isolation and do not explicitly leverage the manifold structure of unlabeled data, which can limit robustness and interpretability. This paper proposes Anomaly-Aware Graph-based Semi-Supervised Deep Support Vector Data Description (AAG-DSVDD), a boundary-focused deep one-class approach that couples a DeepSAD-style hypersphere with a label-aware latent k-nearest neighbor (k-NN) graph. The method combines a soft-boundary enclosure for labeled normals, a margin-based push-out for labeled anomalies, an unlabeled center-pull, and a k-NN graph regularizer on the squared distances to the center. The resulting graph term propagates information from scarce labels along the latent manifold, aligns anomaly scores of neighboring samples, and supports sample-level explanations through graph neighborhoods, while test-time scoring remains a single distance-to-center computation. On a controlled two-dimensional synthetic dataset, AAG-DSVDD achieves a mean F1-score of 0.88±0.02 across ten random splits, improving on the strongest baseline by about 0.12 absolute F1. In a multi-sensor fire monitoring case study, AAG-DSVDD reduces the average fire starting time accuracy to approximately 473 seconds (about 30% improvement over DeepSAD), while keeping the average pre-fire false-alarm rate below 1% and avoiding persistent pre-fire alarms. These results indicate that graph-regularized deep one-class boundaries offer an effective and interpretable framework for semi-supervised anomaly detection under realistic label budgets.