This project implements a system for detecting anomalies in time series data collected from Prometheus. It uses an LSTM (Long Short-Term Memory) autoencoder model built with TensorFlow/Keras to learn ...

Acceldata, a leading provider of data observability and agentic data management solutions, is announcing a new capability designed to amplify the power of agentic reasoning within the company's xLake ...

“Anomaly detection is the holy grail of cyber detection where, if you do it right, you don’t need to know a priori the bad thing that you’re looking for,” Bruce Potter, CEO and founder of ...

Key Concepts: LSTM (Long Short-Term Memory): A type of Recurrent Neural Network (RNN) ideal for time-series data. Autoencoder: A neural network used for unsupervised learning of data representations ...

Log-based anomaly detection has become essential for improving software system reliability by identifying issues from log data. However, traditional deep learning methods often struggle to interpret ...

The task of anomaly detection is to separate anomalous data from normal data in the dataset. Models such as deep Convolutional AutoEncoder (CAE) and deep support vector data description (SVDD) have ...

By pushing computing resources from the cloud to the network edge close to mobile users, mobile edge computing (MEC) enables low latency for a wide variety of applications. Nevertheless, in dynamic ...