Group Anomaly Detection
3 papers with code • 0 benchmarks • 1 datasets
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CHAD
Latest papers with no code
GADformer: A Transparent Transformer Model for Group Anomaly Detection on Trajectories
Hence, this paper introduces GADformer, a BERT-based model for attention-driven GAD on trajectories in unsupervised and semi-supervised settings.
New Methods and Datasets for Group Anomaly Detection From Fundamental Physics
The identification of anomalous overdensities in data - group or collective anomaly detection - is a rich problem with a large number of real world applications.
Kullback-Leibler Divergence-Based Out-of-Distribution Detection with Flow-Based Generative Models
For point-wise anomaly detection, our method achieves 90. 7\% AUROC on average and outperforms the baseline by 5. 2\% AUROC.
Finding Rats in Cats: Detecting Stealthy Attacks using Group Anomaly Detection
Our approach is to build a neural network model utilizing Adversarial Autoencoder (AAE-$\alpha$) in order to detect the activity of an attacker who leverages off-the-shelf tools and system applications.
Correlated Anomaly Detection from Large Streaming Data
Correlated anomaly detection (CAD) from streaming data is a type of group anomaly detection and an essential task in useful real-time data mining applications like botnet detection, financial event detection, industrial process monitor, etc.
Detecting Clusters of Anomalies on Low-Dimensional Feature Subsets with Application to Network Traffic Flow Data
In this work, we develop a group anomaly detection (GAD) scheme to identify the subset of samples and subset of features that jointly specify an anomalous cluster.
GLAD: Group Anomaly Detection in Social Media Analysis- Extended Abstract
Existing group anomaly detection approaches rely on the assumption that the groups are known, which can hardly be true in real world social media applications.
One-Class Support Measure Machines for Group Anomaly Detection
We propose one-class support measure machines (OCSMMs) for group anomaly detection which aims at recognizing anomalous aggregate behaviors of data points.
One-Class Support Measure Machines for Group Anomaly Detection
We propose one-class support measure machines (OCSMMs) for group anomaly detection which aims at recognizing anomalous aggregate behaviors of data points.
Group Anomaly Detection using Flexible Genre Models
We evaluate the effectiveness of FGM on both synthetic and real data sets including images and turbulence data, and show that it is superior to existing approaches in detecting group anomalies.
