McNdroid: A Longitudinal Multimodal Benchmark for Robust Drift Detection in Android Malware

Dataset Description

McNdroid is a large-scale, longitudinal, multimodal Android malware detection dataset designed to benchmark concept drift robustness. It spans samples collected from 2013 to 2025 and provides three complementary modalities: static feature vectors, API call graphs (GML), and JSON-based behavioral representations. The dataset also includes a rich metadata CSV and per-vendor family-level verdicts, supporting fine-grained label analysis and multi-label learning.

Dataset Summary

• Modalities: Static features (NPZ), API call graphs (GML), JSON behavioral features
• Time span: 2013–2025
• Total size: ~10.9 GB
• Splits: Train/test per year with temporal evaluation protocols
• Labels: Binary (malware/benign) and multi-vendor family-level verdicts

Supported Tasks

• Android malware detection (binary classification)
• Concept drift detection and temporal robustness evaluation
• Multi-modal learning for malware analysis
• Graph-based malware classification

Dataset Structure

Repository Layout

McNdroid/
├── README.md
├── metadata.csv                         # Sample-level metadata (~87 MB)
├── vendor_family_wide_verdict.csv       # Multi-vendor family verdicts (~770 MB)
├── data_feature/                        # Static feature modality
│   └── processed_data/
│       └── init_2013/
│           ├── 2013/
│           │   ├── train_X.npz          # Training feature matrix (sparse)
│           │   ├── test_X.npz           # Test feature matrix (sparse)
│           │   ├── train_meta.npz       # Training labels and metadata
│           │   ├── test_meta.npz        # Test labels and metadata
│           │   ├── vocab.json           # Feature vocabulary
│           │   ├── selector_meta.json   # Feature selector metadata
│           │   └── split_meta.json      # Split statistics
│           ├── 2014/
│           ├── ...
│           └── 2025/
├── gml_feature/                         # API call graph modality (GML files)
│   └── processed_data/
│       └── ...
├── json_feature/                        # JSON behavioral feature modality
│   └── processed_data/
│       └── ...

Data Fields

metadata.csv

Contains per-sample metadata including SHA256 hashes, collection timestamps, labels, and source information.

vendor_family_wide_verdict.csv

Contains malware family labels from multiple antivirus vendors, enabling multi-label and label-noise research.

Static Features (data_feature/)

Each year folder contains:

• train_X.npz / test_X.npz: Sparse feature matrices in NumPy compressed format
• train_meta.npz / test_meta.npz: Associated labels and sample metadata
• vocab.json: Feature name vocabulary mapping
• selector_meta.json: Feature selection metadata
• split_meta.json: Train/test split statistics

Graph Features (gml_feature/)

API call graphs stored in GML format, organized by year. Each graph represents inter-procedural API call relationships extracted via static analysis.

JSON Features (json_feature/)

Behavioral feature representations stored as JSON files, organized by year.

Dataset Creation

Source Data

Samples were collected from public malware repositories and benign application stores spanning 2013–2025. Each sample was processed through a static analysis pipeline to extract permissions, API calls, intents, and other manifest and bytecode-level features.

Annotations

Labels are derived from VirusTotal multi-scanner verdicts. The vendor_family_wide_verdict.csv file preserves per-vendor family attributions to support research on label noise and disagreement.

Considerations for Using the Data

Social Impact

This dataset is intended for defensive cybersecurity research. It should not be used to develop offensive malware capabilities.

Licensing

This dataset is released under the Creative Commons Attribution 4.0 International License (CC-BY-4.0).

Usage

Loading Static Features

import numpy as np
import json

# Load a specific year's training data
train_X = np.load("data_feature/processed_data/init_2013/2013/train_X.npz", allow_pickle=True)
train_meta = np.load("data_feature/processed_data/init_2013/2013/train_meta.npz", allow_pickle=True)

with open("data_feature/processed_data/init_2013/2013/vocab.json") as f:
    vocab = json.load(f)

Loading Metadata

import pandas as pd

metadata = pd.read_csv("metadata.csv")
verdicts = pd.read_csv("vendor_family_wide_verdict.csv")

Citation

If you use this dataset, please cite:

[More Information Needed]

Contact

For questions or issues, please open a discussion on the Community tab.