Privacy-Preserving Machine Learning for Encrypted Traffic Classification in Secure Cloud Services

Encrypted traffic classification has emerged as a critical component of modern network management and cloud security services. While Virtual Private Networks (VPNs) ensure user privacy by encrypting communications, this encryption also complicates traditional traffic identification. Recent research demonstrates that Machine Learning (ML) and Deep Learning (DL) techniques can effectively classify VPN versus non-VPN traffic even without payload inspection. However, to align with trustworthy cloud service requirements, these ML/DL approaches must also preserve user privacy and assure security. In this paper, we review the state-of-the-art ML/DL methods for encrypted VPN traffic classification, emphasizing techniques that enhance trust, including privacy-preserving federated learning, adversarially robust models, and explainable AI. In addition, this work aims to discover the most significant features affecting the VPN classification and identifying the best-performing ML and DL models on available VPN classification datasets. We include studies that focused on characterizing the VPN traffic besides classifying the secure traffic into VPN and non-VPN.