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.