Enhancing encrypted HTTPS traffic classification based on stacked deep ensembles models.

ABSTRACT: The classification of encrypted HTTPS traffic is a critical task for network management and security, where traditional port or payload-based methods are ineffective due to encryption and evolving traffic patterns. This study addresses the challenge using the public Kaggle dataset (145,671 flows, 88 features, six traffic categories: Download, Live Video, Music, Player, Upload, Website). An automated preprocessing pipeline is developed to detect the label column, normalize classes, perform a stratified 70/15/15 split into training, validation, and testing sets, and apply imbalance-aware weighting. Multiple deep learning architectures are benchmarked, including DNN, CNN, RNN, LSTM, and GRU, capturing different spatial and temporal patterns of traffic features. Experimental results show that

SUBMITTER: Elshewey AM 

PROVIDER: S-EPMC12511608 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature