Enhancing Malware Detection with Malware-BERT: A Hybrid Approach Using Multi-Head Attention
Bibliographic record
Abstract
The growing complexity of malware poses an intensifying risk to cybersecurity, especially with strategies formulated to circumvent conventional detection mechanisms. Signature-based and heuristic-based methodologies frequently prove inadequate in detecting innovative or evasive malware, hence forcing the advancement of more sophisticated detection techniques. This study presents Malware-BERT, a model explicitly engineered to identify evasive malware concealed within PDF documents. The newly created dataset, Evasive-PDFMal2022, comprises 5,557 malicious and 4,468 benign samples, and the model utilizes BERT embeddings together with multi-head attention methods to improve detection efficacy. The research illustrates the efficacy of Malware-BERT, attaining an accuracy of 96.3% with the RoBERTa model. These findings underscore the efficacy of hybrid BERT-based designs in enhancing cybersecurity measures against sophisticated PDF-based malware.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".