Multi-Head Attention Based Malware Detection with Byte-Level Representation
Bibliographic record
Abstract
Machine learning (ML)-based malware detection plays a crucial role in cyber-security by enabling the identification of potential malware threats without relying solely on predefined signatures or rules. Conventional ML approaches require a feature engineering step to analyze and convert collected data (e.g., captured network traffic and malware programs) into a format suitable for model training and prediction. However, this particular step typically requires a considerable depth of domain-specific expertise and also adds additional complexity to the learning process. To mitigate this limitation, we propose to perform malware detection directly based on the byte-level representation of malware data. We employ a byte embedding layer to convert byte sequences into higher-dimension representations. Then, we employ the multi-head attention technique to capture their correlation before forwarding the output to a fully connected deep neural network for malware detection. Extensive experiments on multiple datasets with diverse file formats demonstrated the superior performance of our proposed method. Additionally, we performed an ablation study on the role of the byte-embedding layer to show that our approach does not depend on a high embedding dimension for strong predictive performance, which helps reduce training complexity.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".