Building Transparency in Deep Learning-Powered Network Traffic Classification: A Traffic-Explainer Framework

Abstract

Recent advancements in deep learning have significantly enhanced the performance and efficiency of traffic classification in networking systems. However, the lack of transparency in their predictions and decision-making has made network operators reluctant to deploy DL-based solutions in production networks. To tackle this challenge, we propose Traffic-Explainer, a model-agnostic and input-perturbation-based traffic explanation framework. By maximizing the mutual information between predictions on original traffic sequences and their masked counterparts, Traffic-Explainer automatically uncovers the most influential features driving model predictions. Extensive experiments demonstrate that Traffic-Explainer improves upon existing explanation methods by approximately 42%. Practically, we further apply Traffic-Explainer to identify influential features and demonstrate its enhanced transparency across three critical tasks: application classification, traffic localization, and network cartography. For the first two tasks, Traffic-Explainer identifies the most decisive bytes that drive predicted traffic applications and locations, uncovering potential vulnerabilities and privacy concerns. In network cartography, Traffic-Explainer identifies submarine cables that drive the mapping of traceroute to physical path, enabling a traceroute-informed risk analysis.