Anomaly Detection with Autoencoders and GANs

Deep learning-based anomaly detection methods include:

• Autoencoders: Train on normal network behavior and measure reconstruction error. High reconstruction error indicates an anomaly (potential attack).
• Generative Adversarial Networks (GANs): The generator produces synthetic cyber threats, improving classifier robustness.
• Hybrid Models: Combining LSTM autoencoders with CNN-based feature extraction for better precision in detecting network anomalies.