Detecting out-of-distribution (OOD) samples is important for deploying machine learning
models in safety-critical applications such as autonomous driving and robot-assisted …

In real-world scenarios, achieving domain adaptation and generalization poses significant
challenges, as models must adapt to or generalize across unknown target distributions …

Data-driven methodologies have exhibited remarkable performance in identifying various
cyber threats. However, obtaining well-labeled training samples is enormously expensive …

This study evaluates state-of-the-art multivariate time series anomaly detection methods on
Network Intrusion Detection System (NIDS) datasets converted into time series data. We find …