Optimizing multi-objective strategies for enhanced Tor De-anonymization
TL;DR
This work model joint node–technique selection as a tri-objective problem balancing attack gain (AP), attack impact (AI), and attack cost (AC) and selects a preference-aware compromise with VIKOR to support better de‑anonymization.
Abstract
Tor employs multi-layer encryption and three-hop circuits to provide low-latency anonymity. While indispensable for privacy, these same properties can also be misused to conceal illicit activity. This dual-use nature makes effective de‑anonymization essential under appropriate, policy-bounded oversight, so that harmful behavior can be uncovered without undermining legitimate use. Yet de‑anonymization is not free: taking nodes offline and deploying honeypots consumes significant resources, increases exposure, and risks degrading network availability. Prior work faces two limitations: (i) it decouples the choice of which node to target from which method to apply, overlooking their strong coupling; and (ii) it often evaluates effectiveness with narrow, single-effect proxies, neglecting collateral network impact and operational cost. To support better de‑anonymization, we model joint node–technique selection as a tri-objective problem balancing attack gain (AP), attack impact (AI), and attack cost(AC). For each feasible node–method pair we compute these three metrics, extract the Pareto set, prune with ϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon$$\end{document}-constraints, and select a preference-aware compromise with VIKOR. In a Docker-orchestrated testbed, this Pareto-first pipeline achieves about +50%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$+50\%$$\end{document} higher attack gain and roughly -29%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-29\%$$\end{document} lower attack Impact and attack cost compared with random selection.