A Dynamic Website Fingerprinting Defense by Emulating Spatio-Temporal Traffic Features
Dongfang Zhang, Chen Rao, Jianan Huang, Lei Guan, Manjun Tian, Weiwei Liu · Electronics · 2025
Website fingerprinting (WF) attacks analyze encrypted network traffic to exploit side-channel features such as packet sizes, inter-packet timings, and burst patterns, enabling adversaries to infer users’ browsing activities and posing persistent privacy threats even under encryption protocols like TLS. Existing WF defenses primarily rely on static perturbations of coarse statistical features, which fail to reproduce the multi-scale spatio-temporal dynamics of website traffic and are increasingly ineffective against modern deep learning-based classifiers. To address this challenge, we propose WFD-EST, a website fingerprinting defense framework that dynamically emulates spatio-temporal traffic characteristics for fine-grained obfuscation. WFD-EST constructs a multi-scale traffic representation that captures both packet-level dynamics and burst-level correlations. A diffusion-based generator, guided by a fine-tuned large-scale discriminator, synthesizes realistic target traffic templates that preserve structural consistency while reflecting temporal diversity. Based on these templates, a burst-aware manipulation module performs packet padding, insertion, and delay operations to align source flows with target spatio-temporal patterns, generating traffic indistinguishable from real target flows. Evaluations on a real-world dataset comprising 15,000 encrypted samples from three representative websites show that WFD-EST consistently outperforms two state-of-the-art defenses, reducing classification F1 scores by 0.082–0.144 while lowering bandwidth and time overheads by at least 0.086 and 0.054, respectively.