Given a long list of anomaly detection algorithms developed in the last few decades, how do
they perform with regard to (i) varying levels of supervision,(ii) different types of anomalies …

With the rapid development of Internet of Things technologies, the next generation traffic
monitoring infrastructures are connected via the web, to aid traffic data collection and …

Anomaly detection is defined as discovering patterns that do not conform to the expected
behavior. Previously, anomaly detection was mostly conducted using traditional shallow …

Graph anomaly detection (GAD) has wide applications in real-world networked systems. In
many scenarios, people need to identify anomalies on new (sub) graphs, but they may lack …

Graph-based anomaly detection aims to identify anomalous vertices in graph-structured
data. It relies on the ability of graph neural networks (GNNs) to capture both relational and …

Outlier detection (OD) is a key learning task for finding rare and deviant data samples, with
many time-critical applications such as fraud detection and intrusion detection. In this work …

The popularity of WiFi technology opens many new attack opportunities for attackers. It is a
common practice to deploy an intrusion detection system to mitigate these attacks. In recent …

Community detection in social networks is of great importance and is used in a variety of
applications such as recommendation systems and targeted advertising. While detecting …

Anomalies in complex industrial processes are often obscured by high variability and
complexity of event data, which hinders their identification and interpretation using process …

Graph-based fraud detection is an important task in many real-world domains such as
insurance, finance, and cybersecurity. Even if existing semi-supervised models have proven …