There are many different types of anomaly detection techniques. This article explains how to use a neural autoencoder implemented using raw C# to find anomalous data items. Compared to other anomaly ...

This project demonstrates how to use an autoencoder neural network for anomaly detection on synthetic data. The autoencoder is built using TensorFlow and Keras, and the data is preprocessed using ...

This project focuses on anomaly detection using autoencoders, a type of neural network designed for unsupervised learning. The autoencoder is trained on normal data to learn its patterns and ...

Abstract: This work aims at analyzing how provenance data can be used in anomaly detection by employing autoencoder networks which is a crucial operation for securing the various sectors through ...

Anomalies in sensor data caused by errors or cyberattacks can cause severe accidents. To address the issue, this study proposed an innovative anomaly detection algorithm, namely the LSTM Autoencoder ...