Toward Effective Mobile Encrypted Traffic Classification through Deep Learning

Traffic Classification (TC), consisting in how to infer applications generating network traffic, is currently the enabler for valuable profiling information, other than being the workhorse for service differentiation/blocking. Further, TC is fostered by the blooming of mobile (mostly encrypted) traffic volumes, fueled by the huge adoption of hand-held devices. While researchers and network operators still rely on machine learning to pursue accurate inference, we envision Deep Learning (DL) paradigm as the stepping stone toward the design of practical (and effective) mobile traffic classifiers based on automatically-extracted features, able to operate with encrypted traffic, and reflecting complex traffic patterns. In this context, the paper contribution is fourfold. First, it provides a taxonomy of the key network traffic analysis subjects where DL is foreseen as attractive. Secondly, it delves into the non-trivial adoption of DL to mobile TC, surfacing potential gains. Thirdly, to capitalize such gains, it proposes and validates a general framework for DL-based encrypted TC. Two concrete instances originating from our framework are then experimentally evaluated on three mobile datasets of human users’ activity. Lastly, our framework is leveraged to point to future research perspectives.