Unsupervised Spatio-Temporal State Estimation for Fine-grained Adaptive Anomaly Diagnosis of Industrial Cyber-physical Systems

arXiv:2403.02616v1 Announce Type: new
Abstract: Accurate detection and diagnosis of abnormal behaviors such as network attacks from multivariate time series (MTS) are crucial for ensuring the stable and effective operation of industrial cyber-physical systems (CPS). However, existing researches pay little attention to the logical dependencies among system working states, and have difficulties in explaining the evolution mechanisms of abnormal signals. To reveal the spatio-temporal association relationships and evolution mechanisms of the working states of industrial CPS, this paper proposes a …

abstract anomaly arxiv attacks attention cs.ai cs.cr cs.lg cs.ni cs.sy cyber dependencies detection diagnosis eess.sy fine-grained industrial multivariate network series state systems temporal time series type unsupervised