arXiv (All)
open access
When Benign Is Unverified: Label-Signal Mismatch and Abstention-First Triage in Graph-Based Memory Forensics
Machine-learning triage for memory forensics is usually validated on corpora whose benign class is asserted by provenance and never verified. We report a validation study of a graph-based triage pipeline for Windows memory images: Volatility 3 output is compiled into typed heterogeneous graphs and s
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Dissecting Agentic Forensics: The Role of Triage, Prompting, and Evidence Arbitration in Open-World Fake Image Detection#1050538
- Dissecting Agentic Forensics: The Role of Triage, Prompting, and Evidence Arbitration in Open-World Fake Image Detection#1028592
- Beyond Generation: Using Multimodal LLMs as Detectors of LLM-Generated Deepfake Facial Images#1357415
- Automated Inference of Graph Transformation Rules#622689
- IRONPROOF: COBOL-to-Python Transpilation with SMT-Based Equivalence Checking#1257964
Record · ID 1537032
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive