Mean Masked Autoencoder with Flow-Mixing for Encrypted Traffic Classification

Computer Science > Cryptography and Security [Submitted on 31 Mar 2026] Mean Masked Autoencoder with Flow-Mixing for Encrypted Traffic Classification Xiao Liu, Xiaowei Fu, Fuxiang Huang, Lei Zhang Network traffic classification using self-supervised pre-training models based on Masked Autoencoders (MAE) has demonstrated a huge potential. However, existing methods are confined to isolated byte-level reconstruction of individual flows, lacking adequate perception of the multi-granularity contextual relationship in traffic. To address this limitation, we propose Mean MAE (MMAE), a teacher-student MAE paradigm with flow mixing strategy for building encrypted traffic pre-training model. MMAE employs a self-distillation mechanism for teacher-student interaction, where the teacher provides unmasked flow-level semantic supervision to advance the student from local byte reconstruction to multi-granularity comprehension. To break the information bottleneck in individual flows, we introduce a dynamic Flow Mixing (FlowMix) strategy to replace traditional random masking mechanism. By constructing challenging cross-flow mixed samples with interferences, it compels the model to learn discriminative representations from distorted tokens. Furthermore, we design a Packet-importance aware Mask Predictor (PMP) equipped with an attention bias mechanism that leverages packet-level side-channel statistics to dynamically mask tokens with high semantic density. Numerous experiments on a number of datasets covering encrypted applications, malware, and attack traffic demonstrate that MMAE achieves state-of-the-art performance. The code is available at this https URL Comments: Project page \url{this https URL} Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Multimedia (cs.MM); Networking and Internet Architecture (cs.NI) Cite as: arXiv:2603.29537 [cs.CR]   (or arXiv:2603.29537v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.29537 Focus to learn more Submission history From: Lei Zhang [view email] [v1] Tue, 31 Mar 2026 10:19:54 UTC (2,554 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-03 Change to browse by: cs cs.AI cs.MM cs.NI References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)