DoHFuse: A Dual-Branch Architecture with DMAGLSTM for Website Fingerprinting over DNS over HTTPS/3

DoHFuse's dual-branch architecture outperforms all five baselines (KNN, Random Forest, FCNN+GRU, LSTM, Transformer) across 100-, 200-, and 449-class closed-world settings with standard deviations under 0.5 percentage points, while the vanilla Transformer's standard deviation exceeds 5 percentage points at 449 classes (mean 61.50%). This demonstrates that attention-based models without recurrence-scale adaptations are unsuitable for the burst–gap temporal dynamics of DoH/3, and that multi-scale gating (DMAG-LSTM) is the key architectural driver of robustness.

§V.C; Table II; Figure 6 evaluation website-fingerprintml-classifiertraffic-shape generic

Under DoH/3, EDNS(0) padding and HTTP/3 framing collapse packet-length diversity: more than 99% of request packets fall within 233–350 bytes and more than 99% of response packets fall within 555–777 bytes. Direction-signed length-sequence classifiers (RF, LSTM, CNN) achieve only 67.39%, 8.95%, and 7.43% accuracy respectively on 449 classes — near or below random — confirming that size-based traffic analysis features lose nearly all discriminative power against DoH/3.

§IV.D; §V.B; Table I detection website-fingerprinttraffic-shapeml-classifier generic

DoHFuse achieves 88.05% accuracy (precision 88.56, recall 87.96, F1 87.83) fingerprinting 449 websites from DoH/3 traffic alone, demonstrating that EDNS(0) padding and QUIC/HTTP3 multiplexing are insufficient to prevent website fingerprinting. In the open-world setting, the model achieves AUPRC 0.975 with F1 0.951 (precision 0.906, recall 1.0), meaning every monitored site is detected while maintaining a low false-positive rate.

Abstract; §V.C; §V.E evaluation website-fingerprintml-classifier generic

Inter-arrival time sequences between client DoH/3 request packets retain site-specific temporal structure despite QUIC padding and stream multiplexing, exhibiting characteristic micro-burst and inter-burst gap patterns. Using timing features alone, LSTM achieves 83.75% ± 0.92% accuracy at 449 classes, and DoHFuse with its dual-branch DMAG-LSTM reaches 89.08% ± 0.49%, while length-only LSTM degrades to 8.95% under the same conditions.

§IV.D; §V.B; §V.C; Table II detection website-fingerprinttraffic-shapeml-classifier generic

Websites generating only one or two observable non-Google DNS queries per visit have classification accuracy below 30% in DoHFuse's closed-world evaluation, compared to the model's overall 88.05% on 449 classes. Sites such as ftc.gov and ailawandorder.com fell near zero accuracy, revealing that extremely sparse DNS activity is a natural but uncontrolled defense against timing-based fingerprinting.

§V.D; Figure 7 evaluation website-fingerprintml-classifier generic