Malware detection using different supervised learning methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Malware that can threaten cyber security is now a problem that needs to be addressed. Malware detection has been significantly developed through the use of machine learning techniques. However, simple supervised learning is rarely used for malware detection. The goal of this paper is to compare the accuracy of different supervised learning models in malware detection. The experiment will load a dataset with malware and corresponding characteristics, train four models separately using a decision tree, logistic regression, and Naïve Bayes, and compare their train scores, test scores, and test time. The experimental result shows that the decision tree model has the best in malware detection, following the Logistic regression model and the Gaussian Naïve Bayes model which has similar scores. In contrast, the Multinomial Naïve Bayes model has lower scores. Moreover, the logistic regression model is significantly slower than other models. The results of the experiments reflect the different efficiency and accuracy of different supervised learning methods when used for malware detection. A possible reason for the differences between the methods is due to the complex dataset, and in conclusion, supervised learning could play a crucial role in cyber security.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it