Toward a Robust Detection of PowerShell Malware against Code Mixing and Obfuscation by Using Sentence Transformer and Similarity Learning
Bibliographic record
Abstract
Embedded PowerShell commands or scripts are among the most popular malware payloads. For malware that prioritizes stealthiness, such as fileless malware, PowerShell’s access to Windows API functions without additional libraries makes it useful for evading detection. Detecting malicious PowerShell scripts and commands is an open challenge for proactive endpoint protection due to three major issues: (1) The malicious commands are usually hidden in a long script beyond the processing limit of typical machine learning models. (2) They are usually mixed with bulky benign scripts. (3) Script obfuscation can easily conceal their potential matching signatures. In this article, we introduce a novel model addressing these challenges. It incorporates similarity learning, sentence transformer, sliding window method, and stochastic gradient descent (SGD) classifier. Our key insight is that malicious PowerShell code, particularly when obfuscated, exhibits semantic and statistical deviations from benign administrative usage, and these deviations can be captured by contrastive sentence embeddings without the need for de-obfuscation or handcrafted features. We operate this insight through a Siamese similarity learning framework that improves robustness against Out-of-Vocabulary tokens due to unseen code obfuscation methods. The sliding window method enables the model to handle long scripts, and the SGD classifier evaluates segment-level maliciousness. Our model achieves accuracies of 99.01%, 97.59%, 98.70%, and 99.73% across multiple obfuscated and mixed script benchmarks, outperforming existing baselines by over 30% in all cases. This work demonstrates a scalable and effective strategy for robust PowerShell malware detection in real-world scenarios.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".