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The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.

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Research Article | Open Access |

Sustainable Android Malware Detection Scheme using Deep Learning Algorithm

Author 1: Abdulaziz Alzubaidi
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 12, No. 12 · Published 2021

DOI: https://doi.org/10.14569/IJACSA.2021.01212104

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Abstract

The immense popularity of smartphones has led to the constant use of these devices for productive and entertainment purposes in daily life. Among the different operating systems, the Android system plays a very important role in the development of mobile technology as it is the most popular operating system. This makes it a target for cyberattack, with severe negative effects in terms of monetary and privacy costs. Thus, this study implements a detection scheme using effective deep learning algorithms (LSTM and MLP). Also, it tests their ability to detect malware by employing private and public datasets, with accuracy of over than 99%.

Keywords

Smartphone security machine learning mobile malware classification big data

How to Cite this Article

Alzubaidi, A. (2021). Sustainable Android Malware Detection Scheme using Deep Learning Algorithm. International Journal of Advanced Computer Science and Applications, 12(12). https://doi.org/10.14569/IJACSA.2021.01212104

Alzubaidi, Abdulaziz. "Sustainable Android Malware Detection Scheme using Deep Learning Algorithm." International Journal of Advanced Computer Science and Applications, vol. 12, no. 12, 2021, https://doi.org/10.14569/IJACSA.2021.01212104.

@article{Alzubaidi2021,
  title     = {Sustainable Android Malware Detection Scheme using Deep Learning Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {12},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Abdulaziz Alzubaidi},
  doi       = {10.14569/IJACSA.2021.01212104},
  url       = {https://doi.org/10.14569/IJACSA.2021.01212104}
}

Open Access — licensed under a Creative Commons Attribution 4.0 International License. Unrestricted use, distribution, and reproduction in any medium, even commercially, as long as the original work is properly cited.

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