aiqbench analysis
This research introduces MASCOT-Android, a dataset and automated pipeline designed to identify Android malware source code by analyzing repository documentation using machine learning classifiers. The study demonstrates that character-level TF-IDF features from README files can effectively distinguish malicious repositories with high accuracy, addressing the scarcity of available source code specimens for security analysis. By automating this discovery process on platforms like GitHub, the work provides a scalable method to enhance threat intelligence without relying solely on binary or decompiled artifacts.
MASCOT-Android: A Curated Dataset and Automated Collection Pipeline for Android Malware Source Code Specimens
arXiv:2606.16072v2 Announce Type: replace-cross Abstract: 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.
Abstract: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.
Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
arXiv:2606.16072 [cs.CR]
(or arXiv:2606.16072v2 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2606.16072 Focus to learn more arXiv-issued DOI via DataCite
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