iResolveX: Multi-Layered Indirect Call Resolution via Static Reasoning and Learning-Augmented Refinement
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866918304756531200 |
|---|---|
| author | Santra, Monika Zhang, Bokai Lim, Mark Dasu, Vishnu Asutosh Zeng, Dongrui Tan, Gang |
| author_facet | Santra, Monika Zhang, Bokai Lim, Mark Dasu, Vishnu Asutosh Zeng, Dongrui Tan, Gang |
| contents | Indirect call resolution remains a key challenge in reverse engineering and control-flow graph recovery, especially for stripped or optimized binaries. Static analysis is sound but often over-approximates, producing many false positives, whereas machine-learning approaches can improve precision but may sacrifice completeness and generalization. We present iResolveX, a hybrid multi-layered framework that combines conservative static analysis with learning-based refinement. The first layer applies a conservative value-set analysis (BPA) to ensure high recall. The second layer adds a learning-based soft-signature scorer (iScoreGen) and selective inter-procedural backward analysis with memory inspection (iScoreRefine) to reduce false positives. The final output, p-IndirectCFG, annotates indirect edges with confidence scores, enabling downstream analyses to choose appropriate precision--recall trade-offs. Across SPEC CPU2006 and real-world binaries, iScoreGen reduces predicted targets by 19.2% on average while maintaining BPA-level recall (98.2%). Combined with iScoreRefine, the total reduction reaches 44.3% over BPA with 97.8% recall (a 0.4% drop). iResolveX supports both conservative, recall-preserving and F1-optimized configurations and outperforms state-of-the-art systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17888 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | iResolveX: Multi-Layered Indirect Call Resolution via Static Reasoning and Learning-Augmented Refinement Santra, Monika Zhang, Bokai Lim, Mark Dasu, Vishnu Asutosh Zeng, Dongrui Tan, Gang Software Engineering Cryptography and Security Programming Languages Indirect call resolution remains a key challenge in reverse engineering and control-flow graph recovery, especially for stripped or optimized binaries. Static analysis is sound but often over-approximates, producing many false positives, whereas machine-learning approaches can improve precision but may sacrifice completeness and generalization. We present iResolveX, a hybrid multi-layered framework that combines conservative static analysis with learning-based refinement. The first layer applies a conservative value-set analysis (BPA) to ensure high recall. The second layer adds a learning-based soft-signature scorer (iScoreGen) and selective inter-procedural backward analysis with memory inspection (iScoreRefine) to reduce false positives. The final output, p-IndirectCFG, annotates indirect edges with confidence scores, enabling downstream analyses to choose appropriate precision--recall trade-offs. Across SPEC CPU2006 and real-world binaries, iScoreGen reduces predicted targets by 19.2% on average while maintaining BPA-level recall (98.2%). Combined with iScoreRefine, the total reduction reaches 44.3% over BPA with 97.8% recall (a 0.4% drop). iResolveX supports both conservative, recall-preserving and F1-optimized configurations and outperforms state-of-the-art systems. |
| title | iResolveX: Multi-Layered Indirect Call Resolution via Static Reasoning and Learning-Augmented Refinement |
| topic | Software Engineering Cryptography and Security Programming Languages |
| url | https://arxiv.org/abs/2601.17888 |