VADViT:Vision Transformer-Driven Memory Forensics for Malicious Process Detection and Explainable Threat Attribution
Bibliographic record
Abstract
Modern malware's increasing complexity limits traditional signature and heuristic-based detection, necessitating advanced memory forensic techniques. Machine learning offers potential but struggles with outdated feature sets, large memory data handling, and forensic explainability. To address these challenges, we propose VADViT, a vision-based transformer model that detects malicious processes by analyzing Virtual Address Descriptor (VAD) memory regions. VADViT converts these structures into Markov, entropy, and intensity-based images, classifying them using a Vision Transformer (ViT) with self-attention to enhance detection accuracy. We also introduce BCCC-MalMem-SnapLog-2025, a dataset logging process identifier (PID) for precise VAD extraction without dynamic analysis. Experimental results show 99% accuracy in binary classification and a 93% macro-average F1 score in multi-class detection. Additionally, attention-based sorting improves forensic analysis by ranking the most relevant malicious VAD regions, narrowing down the search space for forensic investigators.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".