Neural Network Architectures for Threat Detection

Various deep learning architectures are employed for cybersecurity threat detection:

• Convolutional Neural Networks (CNNs): Used for image-based threat detection, such as phishing website classification.
• Recurrent Neural Networks (RNNs) & LSTMs: Effective for sequence-based analysis, such as monitoring system logs and identifying abnormal behavior.
• Autoencoders: Used for anomaly detection by learning the normal behavior of a system and flagging deviations.
• Transformer Models: Applied in threat intelligence processing, analyzing large datasets for attack correlation.