Isolation Forest detects anomalies by isolating observations. It builds binary trees (called iTrees) by recursively ...

Article citations More>> Hajjouz, S. and Avksentieva, N. (2022) Autoencoder-Based Anomaly Detection for IoT DDoS Attack Identification. Journal of Network Security, 24, 512-525. has been cited by the ...

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 ...

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 through ...

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 ...

The Data Science Lab Data Anomaly Detection Using a Neural Autoencoder with C# Dr. James McCaffrey of Microsoft Research tackles the process of examining a set of source data to find data items that ...

As there is no existing anomaly detection network for event cameras, we compare our method with state-of-the-art frame-based anomaly detection approaches. This section provides comparison of the ...