A2MAF: Static and Runtime Behavioral Features for Android Malware

Static and runtime behavioral features for AAOS malware research. The dataset is sourced from the AndroZoo collection and is hosted on Kaggle. Its specific size, authorship, and update date are not provided.

Use Cases

• Train malware classification models based on static behavioral features.
• Analyze runtime behavior patterns of Android malware.
• Benchmark detection algorithms using features from the AndroZoo ecosystem.
• Study the evolution of malware techniques through combined static and dynamic analysis.

Strengths

• Focuses on both static and runtime behavioral features, enabling multi-faceted analysis.
• Part of the established AndroZoo malware research ecosystem.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

AndroZoo