Heterogeneous Cross-Chain Transaction Tracing for Solana Bridges via Candidate-Set Selective Decision
SolTracer traces cross-chain transactions onto Solana bridges, improving open-world association F1 by 20.16% over the strongest baseline for illicit-fund tracing.
The paper formalizes four Solana-bound cross-chain transaction modes and proposes SolTracer, which maps heterogeneous execution semantics into a unified event space and uses candidate-set selective decision-making with abstention when valid targets are absent. In the challenging open-world setting with a 50% TA ratio, SolTracer improves F1 by 20.16% over the strongest baseline. An empirical study of real-world transfers examines count-value divergence across bridge mechanisms, cross-asset shifts, and decoupling between on-chain settlement and explorer visibility.
- Formalizes four Solana-bound cross-chain transaction modes
- Improves open-world F1 by 20.16% over the strongest baseline at 50% TA ratio
- Maps heterogeneous chain semantics into a unified event space with abstention
- Empirical analysis covers bridge count-value divergence and settlement-visibility decoupling
Full article201 words · extracted from arxiv.org · click to collapse
Solana is a rapidly growing high-throughput blockchain platform that has attracted substantial liquidity and user activity. However, this expansion has also drawn the attention of illicit actors, who frequently leverage cross-chain bridges to route illicit funds onto Solana to obfuscate transaction lineage. Unlike EVM-compatible platforms, Solana features distinct execution dynamics and lacks standard event logs, creating severe semantic gaps that prevent existing tracing methods from reliably correlating cross-ledger transactions. In this paper, we formalize four types of Solana-bound cross-chain transaction modes and propose a candidate-set selective decision-based tracing method called SolTracer. SolTracer maps disparate execution semantics into a unified event space and employs candidate-set selective decision-making to reliably associate target transactions while abstaining when valid targets are absent. Extensive experiments demonstrate that SolTracer outperforms state-of-the-art (SOTA) methods across three representative scenarios: closed-world association, open-world association, and cross-source-chain generalization. In particular, under the challenging open-world setting with a 50% TA ratio, SolTracer improves the F1 score by 20.16% over the strongest SOTA baseline. Utilizing SolTracer, we conduct an empirical analysis on real-world cross-chain transfers to investigate ecosystem dynamics. Our analysis explores the stark count-value divergence across bridge mechanisms, the prevalence of cross-asset shifts, and the decoupling between on-chain settlement and explorer visibility.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.11413