The Next Generation of Robust Linux Memory Acquisition Technique via Sequential Memory Dumps at Designated Time Intervals
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
The memory forensics techniques assist digital investigators to identify and detect remaining evidence of the attacks on the compromised system. The accuracy of performing the analysis is depend to the completeness, atomicity, and reliability of the memory acquisition output. Regarding to our research, the most current critical challenges in memory forensics are increasing the size of physical memory, the elapsed time of memory acquisition, malicious tampering and page smearing effects, and anti-forensics techniques. By addressing these challenges, we proposed an approach to determine approximately how much sequential memory acquisition at a designated time-intervals can mitigate them. This mitigation includes reducing I/O operations in memory acquisition to speed it up, diminishing malicious tampering and page smearing effects, and impact of anti-forensics techniques. The results of our experiments on different Linux operating system families show the best interval time for sequential memory acquisition is 3 minutes with the similarity ration between 9% to 23%. The proposed approach is applicable to software-based and hardware-based memory acquisition methods.
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 | 0.001 |
| Open science | 0.001 | 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