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 …
[HTML][HTML] Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives
Enormous amounts of data are being produced everyday by sub-meters and smart sensors
installed in residential buildings. If leveraged properly, that data could assist end-users …
installed in residential buildings. If leveraged properly, that data could assist end-users …
[HTML][HTML] Credit card fraud detection in the era of disruptive technologies: A systematic review
Credit card fraud is becoming a serious and growing problem as a result of the emergence
of innovative technologies and communication methods, such as contactless payment. In …
of innovative technologies and communication methods, such as contactless payment. In …
Anomaly detection for IoT time-series data: A survey
Anomaly detection is a problem with applications for a wide variety of domains; it involves
the identification of novel or unexpected observations or sequences within the data being …
the identification of novel or unexpected observations or sequences within the data being …
Gadbench: Revisiting and benchmarking supervised graph anomaly detection
With a long history of traditional Graph Anomaly Detection (GAD) algorithms and recently
popular Graph Neural Networks (GNNs), it is still not clear (1) how they perform under a …
popular Graph Neural Networks (GNNs), it is still not clear (1) how they perform under a …
An explainable artificial intelligence approach for unsupervised fault detection and diagnosis in rotating machinery
The monitoring of rotating machinery is an essential task in today's production processes.
Currently, several machine learning and deep learning-based modules have achieved …
Currently, several machine learning and deep learning-based modules have achieved …
Fast anomaly identification based on multiaspect data streams for intelligent intrusion detection toward secure industry 4.0
Various cyber attacks often occur in logistics network of the Industry 4.0, which poses a
threat to Internet security. Intrusion detection can intelligently detect anomalous activities …
threat to Internet security. Intrusion detection can intelligently detect anomalous activities …
Pyod: A python toolbox for scalable outlier detection
PyOD is an open-source Python toolbox for performing scalable outlier detection on
multivariate data. Uniquely, it provides access to a wide range of outlier detection …
multivariate data. Uniquely, it provides access to a wide range of outlier detection …
Passban IDS: An intelligent anomaly-based intrusion detection system for IoT edge devices
Cyber-threat protection is today's one of the most challenging research branches of
information technology, while the exponentially increasing number of tiny, connected …
information technology, while the exponentially increasing number of tiny, connected …
Interpretable anomaly detection with diffi: Depth-based feature importance of isolation forest
Anomaly Detection is an unsupervised learning task aimed at detecting anomalous
behaviors with respect to historical data. In particular, multivariate Anomaly Detection has an …
behaviors with respect to historical data. In particular, multivariate Anomaly Detection has an …