Real-time vulnerability detection and anomaly reporting tools enable cybersecurity teams to swiftly identify and neutralize threats before they intensify.

It uses an LSTM (Long Short-Term Memory) autoencoder model built with TensorFlow/Keras to learn normal patterns from your metrics and identify deviations. - GitHub - ...

To make our anomaly detection lightweight, we further design a Light Convolutional Autoencoder (LightCAE) which contains a compressed autoencoder by exploiting tensor factorization to largely compress ...

In this article, the integrated model of the convolutional neural network (CNN) and recurrent autoencoder is proposed for anomaly detection. Simple combination of CNN and autoencoder cannot improve ...

To improve the accuracy of anomaly detection under unbalanced sample conditions, we propose a new semi-supervised anomaly detection method (WCOS) based on semi-supervised clustering, which combines ...

These results indicate that the introduced 2D convolutional autoencoder and multi-sequence, multi-scale asynchronous information extraction methods effectively capture asynchronous correlation ...

The primary question we are trying to answer to is how can convolutional layers be integrated into autoencoder architectures to enhance anomaly detection in spacecraft telemetry data? In efforts to ...

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 ...

Anomaly detection is the process of finding items in a dataset that are different in some way from the majority of the items. For example, you could examine a dataset of credit card transactions to ...