MASCOT-Android: A Curated Dataset and Automated Collection Pipeline for Android Malware Source Code Specimens
This work addresses the scarcity of Android malware source code datasets for security researchers by providing a scalable collection method.
The paper introduces MASCOT-Android, a curated dataset of Android malware source code and an automated pipeline for discovering such code on GitHub. The README-only classifier achieves 96.28% accuracy and 1.06% false positive rate.
Compared with binaries and decompiled code, malware source code more directly reflects the attackers' original intent. However, the scarcity of source code and the high cost of manual review make such datasets difficult to build and maintain. We propose MASCOT-Android, a curated dataset of Android malware source code and an automated collection framework for scalable malware source code discovery on GitHub. A key finding of our work is that repository-level documentation alone provides a strong signal for malware source code collection. Our model extracts character-level TF-IDF features from 8,772 malware and 25,747 benign README documents and trains a LinearSVC classifier to distinguish malware repositories. This README-only model achieves an accuracy of 96.28\% and an FPR of 1.06\% in local evaluation. In addition, the model outputs confidence scores, allowing users to adjust the decision threshold to balance FPR and coverage, which is practical in real-world malware source code collection.