Encrypted Traffic

Encrypted traffic analysis aims to identify applications and detect malicious activity within encrypted network communications without decryption, posing significant challenges due to the inherent obfuscation. Current research focuses on developing machine learning models, including deep learning architectures like neural networks and graph neural networks, to classify encrypted traffic based on features extracted from network metadata and traffic patterns. These advancements are crucial for network security, enabling improved intrusion detection, quality of service management, and user privacy protection in the face of widespread encryption.