The methodology integrates supervised (Random Forest), unsupervised (Isolation Forest), and deep learning (LSTM autoencoder) techniques, leveraging federated learning for edge deployment and ...

The methodology integrates supervised (Random Forest), unsupervised (Isolation Forest), and deep learning (LSTM autoencoder) techniques, leveraging federated learning for edge deployment and ...

To address imbalanced data challenges in intrusion detection, we propose SA-LCA, integrating an improved stacked autoencoder with LSTM-CNN-Attention. Preprocessing uses one-hot encoding and ...

This study presents an integrated framework combining 1D-CNN-LSTM-Autoencoder-based anomaly detection with identity authentication using machine learning classifiers. The 1D-CNN-LSTM Autoencoder ...