Network-based Intrusion Detection System (NIDS) forms the frontline defence against
network attacks that compromise the security of the data, systems, and networks. In recent …

Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …

Abstract Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of
machine learning systems. For instance, in autonomous driving, we would like the driving …

Anomaly detection, aka outlier detection or novelty detection, has been a lasting yet active
research area in various research communities for several decades. There are still some …

Anomalies represent deviations from the intended system operation and can lead to
decreased efficiency as well as partial or complete system failure. As the causes of …

Machine learning models often encounter samples that are diverged from the training
distribution. Failure to recognize an out-of-distribution (OOD) sample, and consequently …

High-accuracy and real-time semi-supervised image surface defect detection is extensively
needed in industrial scenarios. However, existing methods do not provide a good balance …

Anomaly detection is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …

Obtaining expert labels in clinical imaging is difficult since exhaustive annotation is time-
consuming. Furthermore, not all possibly relevant markers may be known and sufficiently …

Real-world machine learning systems need to analyze novel testing data that differs from the
training data. In K-way classification, this is crisply formulated as open-set recognition, core …