← 研踪 PaperTrailarXiv:2611.03323 · v1
A Symbolic-Neural Framework for Context-Aware Evidence Extraction from Mobile SQLite Databases
首次提交 2026-11-02 23:47
版本更新 2026-11-02 23:47
雷达观测 2026-11-04 23:41
摘要
Reliable forensic evidence extraction from mobile SQLite databases is challenging because application schemas are undocumented, evolve across releases, and differ across operating systems, making schema-specific methods brittle and difficult to maintain. We present SNEEF, a symbolic-neural framework for context-aware evidence extraction that encodes each SQLite row as context-value pairs with standardized provenance metadata and column labels, uses a language model to recognize nine forensic entity types, and binds each accepted entity to its source table and row through the PA-EEP provenance protocol. We evaluate SNEEF against examiner-verified ground truth from five corpora built from public Android and iOS device images, spanning multiple application versions. SNEEF achieves an F1 score of 0.967 for evidence entities extracted from SQLite row content, excluding application names, compared with 0.728 for a context-free configuration, with consistent gains across all corpora. We also introduce an Evidence Failure Mode taxonomy to characterize why extraction errors occur.
作者备注：10 pages, 1 figure, 5 tables. Accepted at the 60th Hawaii International Conference on System Sciences (HICSS-60), January 2027
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v1 · 2026-11-02 23:47 · 最新观测版本
A Symbolic-Neural Framework for Context-Aware Evidence Extraction from Mobile SQLite Databases
Reliable forensic evidence extraction from mobile SQLite databases is challenging because application schemas are undocumented, evolve across releases, and differ across operating systems, making schema-specific methods brittle and difficult to maintain. We present SNEEF, a symbolic-neural framework for context-aware evidence extraction that encodes each SQLite row as context-value pairs with standardized provenance metadata and column labels, uses a language model to recognize nine forensic entity types, and binds each accepted entity to its source table and row through the PA-EEP provenance protocol. We evaluate SNEEF against examiner-verified ground truth from five corpora built from public Android and iOS device images, spanning multiple application versions. SNEEF achieves an F1 score of 0.967 for evidence entities extracted from SQLite row content, excluding application names, compared with 0.728 for a context-free configuration, with consistent gains across all corpora. We also introduce an Evidence Failure Mode taxonomy to characterize why extraction errors occur.
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