Data Augmentation by AutoEncoders for Unsupervised Anomaly Detection

no code implementations • 21 Dec 2019 • Kasra Babaei, ZhiYuan Chen, Tomas Maul

The augmented data is used for training the OCC algorithms.

Data Augmentation Unsupervised Anomaly Detection

AEGR: A simple approach to gradient reversal in autoencoders for network anomaly detection

no code implementations • 21 Dec 2019 • Kasra Babaei, Zhi Yuan Chen, Tomas Maul

Anomaly detection is referred to as a process in which the aim is to detect data points that follow a different pattern from the majority of data points.

Anomaly Detection Data Augmentation

Detecting Point Outliers Using Prune-based Outlier Factor (PLOF)

no code implementations • 5 Nov 2019 • Kasra Babaei, ZhiYuan Chen, Tomas Maul

Recently, density-based methods have shown promising results, particularly among which Local Outlier Factor (LOF) is arguably dominating.

Anomaly Detection Outlier Detection