Binghui Wu, Dinil Mon Divakaran, Mohan Gurusamy · 2026 · arXiv
DOI: 10.48550/arXiv.2610.27456 · open-access PDF
Klasifikator lalu lintas terenkripsi tahap awal membuat keputusan hanya dari beberapa paket pertama koneksi, memungkinkan klasifikasi latensi rendah tetapi meningkatkan kekhawatiran robustitas di bawah morfinasi lalu lintas yang realistis. Metode penciptaan lalu lintas lawan yang ada sering mengasumsikan aliran lengkap, gangguan tingkat fitur, atau umpan balik runtime iteratif, yang tidak sesuai dengan keterbatasan eksekusi tahap awal. Kami menyajikan NetGuise, kerangka kerja morfinasi lalu lintas yang ditargetkan yang menghasilkan padding tingkat paket dan tindakan penundaan non-negatif sambil mempertahankan arah paket, pemesanan, dan koet paket
Machine translation (Meta M2M100); the English original follows.
Early-stage encrypted traffic classifiers make decisions from only the first few packets of a connection, enabling low-latency classification but raising robustness concerns under realistic traffic morphing. Existing adversarial traffic generation methods often assume complete flows, feature-level perturbations, or iterative runtime feedback, which do not match the constraints of early-stage execution. We present NetGuise, a target-conditioned traffic morphing framework that generates packet-level padding and non-negative delay actions while preserving packet direction, ordering, and packet co
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