Memory Analysis for Malware Detection: A Comprehensive Survey Using the OSCAR Methodology
Bibliographic record
Abstract
The steady growth of malware over the years has now sharply escalated, with a 30% surge in global cyberattacks in 2024. This rise demands advanced detection, as traditional methods often miss sophisticated or fileless malware. Memory analysis detects traces left by any malware in volatile memory, revealing runtime behaviors, privilege escalation attempts, and active processes. An examination of prior research shows that existing surveys on memory analysis have significant gaps, as none provide a comprehensive overview of the field. To address these gaps, this survey systematically proposes key research questions and addresses them using the OSCAR (Obtain, Strategize, Collect, Analyze, Report) methodology. Memory acquisition techniques and tools have been discussed with the most diverse taxonomy provided to the best of our knowledge. Furthermore, forensic methods, tools, and studies are categorized into four distinct approaches, with a comprehensive taxonomy at the end. We also evaluated and ranked memory dump datasets using our proposed scoring system. Finally, the survey covers malware detection methods, examining both machine learning and traditional approaches and their accuracy, benefits, drawbacks, and challenges. This survey aims to provide a comprehensive and up-to-date overview of the field of memory analysis, with a focus on detecting malicious activities.
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.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".