An unsupervised autoencoder approach achieves moderate success for anomaly detection (accuracy = 0.881) but struggles with recall (0.070). These findings highlight the trade-off between detection ...

Jang et al. [36] introduced a Convolutional Variational Autoencoder (CVAE) that models the variability in ECG patterns through learned latent distributions, facilitating clustering and anomaly ...

Anomaly detection can be powerful in spotting cyber incidents, but experts say CISOs should balance traditional signature-based detection with more bespoke methods that can identify malicious ...

In this paper, we propose a novel approach named Multi-scale Convolution Fusion and Memory-augmented Adversarial AutoEncoder (MCFMAAE) for multivariate time series anomaly detection. It is an ...

Hyperspectral anomaly detection (HAD) plays a vital role in military and civilian applications. However, compared with target detection or classification tasks, HAD is more challenging due to ...

This project is a practice implementation of an autoencoder, The primary use case for this autoencoder is for anomaly detection in sales data, but it can be adapted for other purposes. The autoencoder ...

Thus, we propose an ECG anomaly detection framework (ECG-AAE) based on an adversarial autoencoder and temporal convolutional network (TCN) which consists of three modules (autoencoder, discriminator, ...