The dataset was built based on 1,000 Android application samples (500 Ransomware and 500 (Benign). This dataset includes 314 features; 199 API packages belong to Android API level 27 and 115 permissions belong to different protection levels (normal, signature, and dangerous). Also the dataset includes ID and category(Benign/Ransomware) columns. We counted the occurrences of these features on the collected samples.

If you use this dataset, please cite the following paper:

Faris, H.; Habib, M.; Almomani, I.; Eshtay, M.; Aljarah, I. Optimizing Extreme Learning Machines Using Chains of Salps for Efficient Android Ransomware Detection. Appl. Sci. 2020, 10, 370, doi:  https://doi.org/10.3390/app10113706

BibTeX

@article{Faris_2020,
    title={Optimizing Extreme Learning Machines Using Chains of Salps for Efficient Android Ransomware Detection},
    volume={10},
    ISSN={2076-3417},
    url={http://dx.doi.org/10.3390/app10113706},
    DOI={10.3390/app10113706},
    number={11},
    journal={Applied Sciences},
    publisher={MDPI AG},
    author={Faris, Hossam and Habib, Maria and Almomani, Iman and Eshtay, Mohammed and Aljarah, Ibrahim},
    year={2020},
    month={May},
    pages={3706}
}

SEL would like to acknowledge the support of Prince Sultan University for paying the Article Processing Charges(APC) of this publication.