A Machine Learning Approach for Malware Detection based on Image Conversion
Bibliographic record
Abstract
Abstract Due to the sophistication of recent malware, classical detection approaches are becoming obsolete. Machine Learning for malware detection has emerged as a new trend and is becoming increasingly effective. Indeed, malware generate a tremendous amount of data that should be analyzed and used to detect them. The aim of this paper is to propose a Machine Learning approach to detect both recent and old malware by converting them into images. This approach, which consists of two phases, is based on Transfer Learning through the use of Convolutional Neural Networks (CNN) that extract features from malware images. These features are used to determine the maliciousness of a particular file. We define six strategies, each one is a combination of two image types (Grayscale and Color) and three CNN architectures (VGG, ResNet and Inception). Experimental evaluation has been done to test these six strategies. The strategy that fulfills the most testing goals is Grayscale + ResNet with a testing accuracy of 90.08\%. Even if the first results are promising, the future work is to automate the fine-tuning of the parameters to go through all possible values and obtain the best ones.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".