An End-to-End Framework for Functionality-Embedded Provenance Graph Construction and Threat Interpretation
Provenance graphs model causal system-level interactions from logs, enabling anomaly detectors to learn normal behavior and detect deviations as attacks. However, existing approaches rely on brittle, manually engineered rules to build provenance graphs, lack functional context for system entities, and provide limited support for analyst investigation. We present Auto-Prov, an adaptive, end-to-end framework that leverages large language models (LLMs) to automatically construct provenance graphs from heterogeneous and evolving logs, embed system-level functional attributes into the graph, enable provenance graph-based anomaly detectors to learn from these enriched graphs, and summarize the ...
| Authors | Kushankur Ghosh, Mehar Klair, Kian Kyars et al. |
|---|---|
| Primary category | cs.CR |
| Published | 17 March 2026 |
| Last updated at source | 17 March 2026 |
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