4 papers
Domain-Prior-Regularized Graph Modeling for Anomaly Detection in Cyber-Physical Systems
Youngseok Hwang, Joonsung Kwon, Geonwoo Lee +1
Anomaly detection on multivariate sensor time series is critical for industrial monitoring of cyber-physical systems (CPS), where even subtle deviations from normal behavior can in…
Redefining Maritime Anomaly Detection via Equation-Grounded Synthetic Anomalies
Youngseok Hwang, Sungho Bae, Dohun Lee +4
Maritime anomaly detection is essential for ensuring maritime safety, security, and efficient traffic management at sea, with Automatic Identification System (AIS) data serving as…
Spatio-Temporal Graphs Beyond Grids: Benchmark for Maritime Anomaly Detection
Jeehong Kim, Youngseok Hwang, Minchan Kim +2
Spatio-temporal graph neural networks (ST-GNNs) have achieved notable success in structured domains such as road traffic and public transportation, where spatial entities can be na…
Adaptive Sparsified Graph Learning Framework for Vessel Behavior Anomalies
Jeehong Kim, Minchan Kim, Jaeseong Ju +3
Graph neural networks have emerged as a powerful tool for learning spatiotemporal interactions. However, conventional approaches often rely on predefined graphs, which may obscure…