An Input-Agnostic Hierarchical Deep Learning Framework for Traffic Fingerprinting


Jian Qu, Xiaobo Ma, and Jianfeng Li, Xi’an Jiaotong University; Xiapu Luo, The Hong Kong Polytechnic University; Lei Xue, Sun Yat-sen University; Junjie Zhang, Wright State University; Zhenhua Li, Tsinghua University; Li Feng, Southwest Jiaotong University; Xiaohong Guan, Xi'an Jiaotong University


Deep learning has proven to be promising for traffic fingerprinting that explores features of packet timing and sizes. Although well-known for automatic feature extraction, it is faced with a gap between the heterogeneousness of the traffic (i.e., raw packet timing and sizes) and the homogeneousness of the required input (i.e., input-specific). To address this gap, we design an input-agnostic hierarchical deep learning framework for traffic fingerprinting that can hierarchically abstract comprehensive heterogeneous traffic features into homogeneous vectors seamlessly digestible by existing neural networks for further classification. The extensive evaluation demonstrates that our framework, with just one paradigm, not only supports heterogeneous traffic input but also achieves better or comparable performance compared to state-of-the-art methods black across a wide range of traffic fingerprinting tasks.

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@inproceedings {287388,
author = {Jian Qu and Xiaobo Ma and Jianfeng Li and Xiapu Luo and Lei Xue and Junjie Zhang and Zhenhua Li and Li Feng and Xiaohong Guan},
title = {An {Input-Agnostic} Hierarchical Deep Learning Framework for Traffic Fingerprinting},
booktitle = {32nd USENIX Security Symposium (USENIX Security 23)},
year = {2023},
isbn = {978-1-939133-37-3},
address = {Anaheim, CA},
pages = {589--606},
url = {},
publisher = {USENIX Association},
month = aug

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