Enhanced Malware Family Classification via Image-Based Analysis Utilizing a Balance-Augmented VGG16 Model
Bibliographic record
Abstract
The escalating sophistication and automation of malware generation techniques have led to an unprecedented proliferation of diverse and potent malicious software, thereby posing a considerable threat to individual, commercial, and digital security.Traditional detection systems often fall short in identifying these evolving threats, underscoring the critical necessity for advanced detection and classification strategies.Herein, we present an innovative deep learning approach for malware image analysis, employing a multi-layer VGG16 model-termed as MLVGGNET.We meticulously assess the performance of our proposed model using a representative dataset, embodying twenty-five distinct malware species, commonly known as the "Malimg" dataset.Our proposed model is evaluated with state-of-the-art techniques.The model's performance metrics include recall, specificity, accuracy, and the F1 score.Our investigations reveal that the MLVGGNET model, particularly when enhanced with class balancing techniques, demonstrates superior performance over existing methodologies.Remarkably, the incorporation of class balancing in the benign class results in highly promising outcomes.Despite its relative simplicity, our proposed MLVGGNET model exhibits robust efficacy in photograph intrusion detection systems, as substantiated by our empirical results.This study thus underscores the potential of our model as an efficient tool for the precise detection and classification of malware, outpacing current approaches.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".