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 …
Deep learning for anomaly detection in multivariate time series: Approaches, applications, and challenges
Anomaly detection has recently been applied to various areas, and several techniques
based on deep learning have been proposed for the analysis of multivariate time series. In …
based on deep learning have been proposed for the analysis of multivariate time series. In …
A survey on security and privacy of federated learning
Federated learning (FL) is a new breed of Artificial Intelligence (AI) that builds upon
decentralized data and training that brings learning to the edge or directly on-device. FL is a …
decentralized data and training that brings learning to the edge or directly on-device. FL is a …
A unifying review of deep and shallow anomaly detection
Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …
the art in detection performance on complex data sets, such as large collections of images or …
Deep learning for anomaly detection: A review
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 …
research area in various research communities for several decades. There are still some …
A survey on anomaly detection for technical systems using LSTM networks
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 …
decreased efficiency as well as partial or complete system failure. As the causes of …
Deep isolation forest for anomaly detection
Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector
in recent years due to its general effectiveness across different benchmarks and strong …
in recent years due to its general effectiveness across different benchmarks and strong …
An evaluation of anomaly detection and diagnosis in multivariate time series
Several techniques for multivariate time series anomaly detection have been proposed
recently, but a systematic comparison on a common set of datasets and metrics is lacking …
recently, but a systematic comparison on a common set of datasets and metrics is lacking …
Learning to detect malicious clients for robust federated learning
Federated learning systems are vulnerable to attacks from malicious clients. As the central
server in the system cannot govern the behaviors of the clients, a rogue client may initiate an …
server in the system cannot govern the behaviors of the clients, a rogue client may initiate an …
[HTML][HTML] Mixing up contrastive learning: Self-supervised representation learning for time series
The lack of labeled data is a key challenge for learning useful representation from time
series data. However, an unsupervised representation framework that is capable of …
series data. However, an unsupervised representation framework that is capable of …