The MalRadar Dataset

Mobile malware detection has attracted massive research effort in our community. A reliable and up-to-date malware dataset is critical to evaluate the effectiveness of malware detection approaches. Essentially, the malware ground truth should be manually verified by security experts, and their malicious behaviors should be carefully labelled. Although there are several widely-used malware benchmarks in our community (e.g., MalGenome, Drebin, Piggybacking and AMD, etc.), these benchmarks face several limitations including out-of-date, size, coverage, and reliability issues, etc.

We make effort to create MalRadar, a growing and up-to-date Android malware dataset using the most reliable way, i.e., by collecting malware based on the analysis reports of security experts. We have crawled all the mobile security related reports released by ten leading security companies, and used an automated approach to extract and label the useful ones describing new Android malware and containing Indicators of Compromise (IoC) information. We have successfully compiled MalRadar, a dataset that contains 4,534 unique Android malware samples (including both apks and metadata) released from 2014 to April 2021 by the time of this paper, all of which were manually verified by security experts with detailed behavior analysis. For more details, please visit https://malradar.github.io/ 

The dataset includes the following files:

(1) sample-info.csv

In this file, we list all the detailed information about each sample, including apk file hash, app name, package name, report family, etc.

(2) malradar.zip

We have packaged the malware samples in chunks of 1000 applications: malradar-0, malradar-1, malradar-2, malradar-3. All the apk files name after the file SHA256.

If your papers or articles used our dataset, please include a citation to our paper:

@article{wang2022malradar,
  title={MalRadar: Demystifying Android Malware in the New Era},
  author={Wang, Liu and Wang, Haoyu and He, Ren and Tao, Ran and Meng, Guozhu and Luo, Xiapu and Liu, Xuanzhe},
  journal={Proceedings of the ACM on Measurement and Analysis of Computing Systems},
  volume={6},
  number={2},
  pages={1--27},
  year={2022},
  publisher={ACM New York, NY, USA}
}