IEEE

Time Series Anomaly Detection Based on Asymmetric Autoencoder and Gaussian Mixture Model

Abstract: Time series data are widely used in domains such as finance, industry, transportation, and healthcare. Anomaly detection in multivariate time series is a challenging unsupervised machine ...
GitHub

autoencoder-anomaly-detection

This repo contains all my Deep Learning semester work, including implementations of FNNs, CNNs, autoencoders, CBOW, and transfer learning. I explored TensorFlow, Keras, PyTorch, and Theano while ...
IEEE

Enhancing Microgrid Security: Web-Based Anomaly Detection Using Autoencoder

Microgrids provide a resilient and efficient alternative to traditional power grids, yet they remain vulnerable to operational anomalies, electrical faults, and cybersecurity threats. This study ...
Scientific Research Publishing

Intelligent Agents in Cybersecurity: Deep Learning to Analyze User Behavior Applying ()

The research aim is to develop an intelligent agent for cybersecurity systems capable of detecting abnormal user behavior ...
GitHub

Time Series Analysis with LSTM, ARIMA, Prophet, and Anomaly Detection

├── src/ # Source code modules │ ├── lstm_model.py # LSTM implementation with PyTorch │ ├── forecasting_models.py # ARIMA, Prophet, and statistical models │ ├── anomaly_detection.py # Anomaly ...