ET-MobileNet: a lightweight encryption traffic classification method

As encryption technologies have become pervasive in safeguarding user privacy, encrypted traffic now constitutes a significant portion of network traffic, posing challenges to conventional rule-based classification methods due to the randomized nature of encrypted contents. In this paper, we deploy the MobileNet within the realm of encrypted traffic classification and propose ET-MobileNet. ET-MobileNet is a lightweight model that specifically designed for devices with limited computational resources. It extracts the packet length sequence and packet arrival interval sequence as features from the session flow, and reshape them into a two-channel picture for input. Our experiments on the ISCXTor2016 dataset encompassing 13 applications indicate that ET-MobileNet can achieve remarkable results while maintaining a low computational cost.