Year: 2026
Type: article
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 $$\epsilon$$ ϵ -constraints, and select a preference-aware compromise with VIKOR. In a Docker-orchestrated testbed, this Pareto-first pipeline achieves about $$+50\%$$ + 50 % higher attack gain and roughly $$-29\%$$ - 29 % lower attack Impact and attack cost compared with random selection.
Source: Cybersecurity
Authors: Yali Yuan, Yuchen Zhang, Ruolin Ma, Liangyi Gong, Guang Cheng
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Awards: G3622784231, G8970236852
OpenAlex ID: W7166689044