Published January 2022 | Version v2
Dataset Restricted

Lightweight, Effective Detection and Characterization of Mobile Malware Families

  • 1. ROR icon Florida Polytechnic University
  • 2. ROR icon Kuwait University

Description

DroidMalVet

DroidMalVet dataset provides a curated collection of 20 code-level metrics extracted from Android malware applications, each labeled with its corresponding malware family. It comprises 24,252 malicious apps categorized into 202 malware families. The metrics capture structural and behavioral properties of the malicious apps and are grouped into four categories: complexity, dimensional, object-oriented, and Android-oriented metrics.

These metrics were extracted by decompiling each malicious application into Smali code and analyzing it with a modified static analysis tool. This dataset enables researchers to study malware family detection, characterization, and evolution with a compact and efficient feature set.

Further details can be found in our paper “Lightweight, Effective Detection and Characterization of Mobile Malware Families” [PDF], IEEE Transactions on Computers, 2022.

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

@ARTICLE {DroidMalVet,
      author={Elish, Karim and Elish, Mahmoud and Almohri, Hussain},
      journal={IEEE Transactions on Computers},
      title={Lightweight, Effective Detection and Characterization of Mobile Malware Families}, 
      year={2022},
      volume={71},
      number={11},
      pages={2982-2995}
}

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Note: Please do not share the data with any others (except your co-authors for the project). We are happy to share with other researchers based upon their requests.

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

@ARTICLE {DroidMalVet,
      author={Elish, Karim and Elish, Mahmoud and Almohri, Hussain},
      journal={IEEE Transactions on Computers},
      title={Lightweight, Effective Detection and Characterization of Mobile Malware Families}, 
      year={2022},
      volume={71},
      number={11},
      pages={2982-2995}
}

 

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Additional details

Additional titles

Subtitle
DroidMalVet Dataset: Android Malware Families