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Mobile Traffic Classification (TC) has become nowadays the enabler for valuable profiling information, other than being the workhorse for service differentiation or blocking. Nonetheless, a main hindrance in the design of accurate classifiers is the adoption of encrypted protocols, compromising the effectiveness of deep packet inspection. Also, the evolving nature of mobile network traffic makes solutions with Machine Learning (ML), based on manually- and expert-originated features, unable to keep its pace. These limitations clear the way to Deep Learning (DL) as a viable strategy to design traffic classifiers based on automatically-extracted features, reflecting the complex patterns distilled from the multifaceted traffic nature, implicitly carrying information in “multimodal” fashion. Multi-modality in TC allows to inspect the traffic from complementary views, thus providing an effective solution to the mobile scenario …