Running prepare_data.py will generate contextual and point anomalies (using the Gaussian Mixture Model and Multivariate Uniform Distribution methods, as described in the paper) and inject them into ...

This repository implements the unsupervised anomaly detection framework presented ... please refer to the paper (we appreciate citations if you find this work useful for your research). Fig. 1: ...

Abstract: Autoencoders have been extensively used in the development of recent anomaly detection techniques. The premise of their application is based on the notion that after training the autoencoder ...

James McCaffrey of Microsoft Research provides full code and step-by-step examples ... The demo program presented in this article uses image data, but the autoencoder anomaly detection technique can ...

Anomaly detection through employing machine learning techniques ... In this work, we construct an infrared and collinear safe autoencoder based on graph neural networks by employing energy-weighted ...

Abstract: Anomaly detection is critical given the raft of cyber attacks in the wireless communications these days. It is thus a challenging task to determine network anomaly more accurately. In this ...

James McCaffrey of Microsoft Research provides full code and step-by-step examples ... The demo program presented in this article uses image data, but the autoencoder anomaly detection technique can ...