The D-CNN-LSTM Autoencoder method optimizes the anomaly detection rate for all of the anomalies, specifically in the case of low magnitude anomalies, enhancing F1-score up to 18.12% in single types of ...

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. The system includes scripts for data ...

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 ...

This project implements a real-time anomaly detection system using an LSTM Autoencoder to analyze continuous data streams. The system is designed to detect unusual patterns, such as exceptional values ...

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 ...

The autoencoder provides an anomaly detection algorithm for radiation treatment planning. It can detect a very small percentage of abnormal plans in a large number of radiotherapy plans with high ...

Anomaly detection for indoor air quality (IAQ) data has become an important area of research as the quality of air is closely related to human health and well-being. However, traditional statistics ...