The paper is a position piece advocating causal graph learning as the basis for interpretable, drift-robust anomaly detection in cyber-physical systems, with a small comparison table as supporting evidence.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Rethinking Spatio-Temporal Anomaly Detection: A Vision for Causality-Driven Cybersecurity
The paper is a position piece advocating causal graph learning as the basis for interpretable, drift-robust anomaly detection in cyber-physical systems, with a small comparison table as supporting evidence.