Comparative Analysis of Machine Learning Models for PDF Malware Detection: Evaluating Different Training and Testing Criteria
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
The proliferation of maliciously coded documents as file transfers increase has led to a rise in sophisticated attacks. Portable Document Format (PDF) files have emerged as a major attack vector for malware due to their adaptability and wide usage. Detecting malware in PDF files is challenging due to its ability to include various harmful elements such as embedded scripts, exploits, and malicious URLs. This paper presents a comparative analysis of machine learning (ML) techniques, including Naive Bayes (NB), K-Nearest Neighbor (KNN), Average One Dependency Estimator (A1DE), Random Forest (RF), and Support Vector Machine (SVM) for PDF malware detection. The study utilizes a dataset obtained from the Canadian Institute for Cyber-security and employs different testing criteria, namely percentage splitting and 10-fold cross-validation. The performance of the techniques is evaluated using F1-score, precision, recall, and accuracy measures. The results indicate that KNN outperforms other models, achieving an accuracy of 99.8599% using 10-fold cross-validation. The findings highlight the effectiveness of ML models in accurately detecting PDF malware and provide insights for developing robust systems to protect against malicious activities.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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