Extraction of forensically sensitive information from windows physical memory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Forensic analysis of physical memory is gaining good attention from experts in the community especially after recent development of valuable tools and techniques. Investigators find it very helpful to seize physical memory contents and perform post-incident analysis of this potential evidence. Most of the research carried out focus on enumerating processes and threads by accessing memory resident objects. To collect case-sensitive information from the extracted memory content, the existing techniques usually rely on string matching. The most important contribution of the paper is a new technique for extracting sensitive information from physical memory. The technique is based on analyzing the call stack and the security sensitive APIs. It allows extracting sensitive information that cannot be extracted by string matching-based techniques. In addition, the paper leverages string matching to get a more reliable technique for analyzing and extracting what we called “application/protocol fingerprints”. The proposed techniques and their implementation target the machines running under the Windows XP (SP1, SP2) operating system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.009 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it