Khashayar Khajavi (Simon Fraser University), Tao Wang (Simon Fraser University)
Website fingerprinting (WF) attacks remain a significant threat to encrypted traffic, prompting the development of a wide range of defenses. Among these, two prominent classes are regularization-based defenses, which shape traffic using fixed padding rules, and supersequence-based approaches, which conceal traces among predefined patterns.
In this work, we present a unified framework for designing an adaptive WF defense that combines the effectiveness of regularization with the provable security of supersequence-style grouping.
The scheme first extracts behavioural patterns from traces and clusters them into $(k,l)$-diverse anonymity sets; an early-time-series classifier (adapted from ECDIRE) then switches from a conservative global set of regularization parameters to the lighter, set-specific parameters.
We instantiate the design as emph{Adaptive Tamaraw}, a variant of Tamaraw that assigns padding parameters on a per-cluster basis while retaining its original information-theoretic guarantee. Comprehensive experiments on public real-world datasets confirm the benefits.
By tuning $k$, operators can trade privacy for efficiency: in its high-privacy mode, Adaptive Tamaraw pushes the bound on any attacker's accuracy below textbf{30%}, whereas in efficiency-centred settings it cuts total overhead by textbf{99} percentage points compared with classic Tamaraw.