Financial fraud detection using graph neural networks: A systematic review
Financial fraud is a persistent problem in the finance industry that may have severe
consequences for individuals, businesses, and economies. Graph Neural Networks (GNNs) …
consequences for individuals, businesses, and economies. Graph Neural Networks (GNNs) …
Addressing heterophily in graph anomaly detection: A perspective of graph spectrum
Graph anomaly detection (GAD) suffers from heterophily—abnormal nodes are sparse so
that they are connected to vast normal nodes. The current solutions upon Graph Neural …
that they are connected to vast normal nodes. The current solutions upon Graph Neural …
Deep anomaly detection on set data: Survey and comparison
Detecting anomalous samples in set data is a problem attracting increased interest due to
novel modalities, such as point-cloud data produced by lidars. Novel methods including …
novel modalities, such as point-cloud data produced by lidars. Novel methods including …
Diga: guided diffusion model for graph recovery in anti-money laundering
With the upsurge of online banking, mobile payment, and virtual currency, new money-
laundering crimes easily conceal in the enormous transaction volume. The traditional rule …
laundering crimes easily conceal in the enormous transaction volume. The traditional rule …
Process-Oriented heterogeneous graph learning in GNN-Based ICS anomalous pattern recognition
L Shuaiyi, K Wang, L Zhang, B Wang - Pattern Recognition, 2023 - Elsevier
Over the past few years, massive penetrations targeting an Industrial Control System (ICS)
network intend to compromise its core industrial processes. So far, numerous advanced …
network intend to compromise its core industrial processes. So far, numerous advanced …
Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection
Traffic time series anomaly detection has been intensively studied for years because of its
potential applications in intelligent transportation. However, classical traffic anomaly …
potential applications in intelligent transportation. However, classical traffic anomaly …
Anomaly detection module for network traffic monitoring in public institutions
It seems to be a truism to say that we should pay more and more attention to network traffic
safety. Such a goal may be achieved with many different approaches. In this paper, we put …
safety. Such a goal may be achieved with many different approaches. In this paper, we put …
Graph-based time-series anomaly detection: A survey
With the recent advances in technology, a wide range of systems continue to collect a large
amount of data over time and thus generate time series. Time-Series Anomaly Detection …
amount of data over time and thus generate time series. Time-Series Anomaly Detection …
Anomaly traffic detection in IoT security using graph neural networks
M Gao, L Wu, Q Li, W Chen - Journal of Information Security and …, 2023 - Elsevier
The number of Internet of Things (IoT) devices is expanding quickly as IoT gradually spreads
to all aspects of life. At the same time, IoT devices have emerged as a new attack medium for …
to all aspects of life. At the same time, IoT devices have emerged as a new attack medium for …
Reinforcement neighborhood selection for unsupervised graph anomaly detection
Unsupervised graph anomaly detection is crucial for various practical applications as it aims
to identify anomalies in a graph that exhibit rare patterns deviating significantly from the …
to identify anomalies in a graph that exhibit rare patterns deviating significantly from the …