The network security protection of Cyber- Physical Systems (CPS), represented by power monitoring systems, is a significant research area. Anomaly detection of multivariate time series generated ...

To this end, we propose a center-aware adversarial autoencoder (CA-AAE) method, which detects anomaly samples by acquiring more compact and discriminative subspace representations. To fully exploit ...

This repository contains a Jupyter Notebook that demonstrates how to perform anomaly detection in time series data using an Autoencoder neural network. The notebook explores a deep learning approach ...

This repository provides an implementation of an anomaly detection system for cell images using autoencoders. The project draws inspiration from the paper "Robust Anomaly Detection in Images using ...

Thus, we propose an ECG anomaly detection framework (ECG-AAE) based on an adversarial autoencoder and temporal convolutional network (TCN) which consists of three modules (autoencoder, discriminator, ...

Data anomaly detection is the process of examining a set of source data to find data items that are different in some way from the majority of the source items. There are many different types of ...

16. Schreyer M, Sattarov T, Schulze C, Bernd R, Damian B. Detection of accounting anomalies in the latent space using adversarial autoencoder neural networks, 2nd KDD workshop on anomaly detection in ...

Data Anomaly Detection Using a Neural Autoencoder with C#. 04/15/2024; Data anomaly detection is the process of examining a set of source data to find data items that are different in some way from ...