A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection
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) …
generated in great volume by both physical sensors and online processes (virtual sensors) …
Deep learning for anomaly detection in time-series data: Review, analysis, and guidelines
As industries become automated and connectivity technologies advance, a wide range of
systems continues to generate massive amounts of data. Many approaches have been …
systems continues to generate massive amounts of data. Many approaches have been …
Tranad: Deep transformer networks for anomaly detection in multivariate time series data
Efficient anomaly detection and diagnosis in multivariate time-series data is of great
importance for modern industrial applications. However, building a system that is able to …
importance for modern industrial applications. However, building a system that is able to …
Graph neural network-based anomaly detection in multivariate time series
Given high-dimensional time series data (eg, sensor data), how can we detect anomalous
events, such as system faults and attacks? More challengingly, how can we do this in a way …
events, such as system faults and attacks? More challengingly, how can we do this in a way …
Learning graph structures with transformer for multivariate time-series anomaly detection in IoT
Many real-world Internet of Things (IoT) systems, which include a variety of Internet-
connected sensory devices, produce substantial amounts of multivariate time-series data …
connected sensory devices, produce substantial amounts of multivariate time-series data …
MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks
Many real-world cyber-physical systems (CPSs) are engineered for mission-critical tasks
and usually are prime targets for cyber-attacks. The rich sensor data in CPSs can be …
and usually are prime targets for cyber-attacks. The rich sensor data in CPSs can be …
MST-GAT: A multimodal spatial–temporal graph attention network for time series anomaly detection
Multimodal time series (MTS) anomaly detection is crucial for maintaining the safety and
stability of working devices (eg, water treatment system and spacecraft), whose data are …
stability of working devices (eg, water treatment system and spacecraft), whose data are …
Adversarial examples: A survey of attacks and defenses in deep learning-enabled cybersecurity systems
Over the last few years, the adoption of machine learning in a wide range of domains has
been remarkable. Deep learning, in particular, has been extensively used to drive …
been remarkable. Deep learning, in particular, has been extensively used to drive …
Self-supervised learning for time series analysis: Taxonomy, progress, and prospects
Self-supervised learning (SSL) has recently achieved impressive performance on various
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …
A survey on deep learning for cybersecurity: Progress, challenges, and opportunities
As the number of Internet-connected systems rises, cyber analysts find it increasingly difficult
to effectively monitor the produced volume of data, its velocity and diversity. Signature-based …
to effectively monitor the produced volume of data, its velocity and diversity. Signature-based …