Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …

Graphs are used widely to model complex systems, and detecting anomalies in a graph is
an important task in the analysis of complex systems. Graph anomalies are patterns in a …

Deep learning on graphs has attracted significant interests recently. However, most of the
works have focused on (semi-) supervised learning, resulting in shortcomings including …

Graph anomaly detection (GAD) is a vital task in graph-based machine learning and has
been widely applied in many real-world applications. The primary goal of GAD is to capture …

Machine learning (ML) and deep learning (DL), in particular, are common tools for anomaly
detection (AD). With the rapid increase in the number of Internet-connected devices, the …

Multivariate time-series anomaly detection is critically important in many applications,
including retail, transportation, power grid, and water treatment plants. Existing approaches …

Over the past few years, anomaly detection, a subfield of machine learning that is mainly
concerned with the detection of rare events, witnessed an immense improvement following …

Detecting abnormal nodes from attributed networks is of great importance in many real
applications, such as financial fraud detection and cyber security. This task is challenging …

Electroencephalogram (EEG) signals are effective tools towards seizure analysis where one
of the most important challenges is accurate detection of seizure events and brain regions in …

Split learning (SL) enables data privacy preservation by allowing clients to collaboratively
train a deep learning model with the server without sharing raw data. However, SL still has …