The Next Generation of Robust Linux Memory Acquisition Technique via Sequential Memory Dumps at Designated Time Intervals
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".