Classifier-Dependent Benefits of Pseudo-Labeling for Semi-Supervised Android Malware Attribution
Detecting and classifying Android malware families remains challenging due to high feature dimensionality, class imbalance, and the high cost of expert-labeled data. Semi-supervised learning (SSL) offers a way to leverage unlabeled samples, but prior works rarely test whether SSL benefits generalize across classifier types or report statistical significance. We present a systematic evaluation of pseudo-labeling across six classifiers (LightGBM, XGBoost, Random Forest, Logistic Regression, MLP, and SVM) on the CICMalDroid 2020 dataset, using five-fold stratified cross-validation and paired t-tests across five labeled ratios (1-20%). We find that SSL benefit is strongly classifier-dependent...
| Authors | Md Rafid Islam, Zahid Hasan, Hafiz Abdur Rahman |
|---|---|
| Primary category | cs.LG |
| Published | 26 August 2026 |
| Last updated at source | 26 August 2026 |
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