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Android Malware Detection Using Artificial Intelligence

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Information and Software Technologies (ICIST 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1979))

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Abstract

Malware poses a significant global cybersecurity challenge, targeting individuals, businesses, institutions, and nations by compromising sensitive information and causing disruptions, incurring substantial costs. Android devices, with relatively lower security measures allowing installations from unknown sources, face notable malware prevalence, creating opportunities for cybercriminals to engage in illicit activities. To address this issue, numerous research studies have focused on harnessing the power of artificial intelligence (AI) to develop effective solutions. Notably, research utilizing the CICMalDroid2020 dataset has achieved promising results by employing Deep Learning and Machine Learning approaches for Android malware detection. However, to the best of our knowledge, no prior studies utilizing this dataset have explored the potential of the Extra-Tree Machine Learning classifier.

In our research, we endeavored to fill this gap by implementing the Extra Tree classifier in conjunction with cross-validation techniques. Additionally, we employed the SelectFrom-Model feature selection method to enhance the accuracy of malware detection. Through our investigation, we found that the ExtraTree classifier exhibited good performances, achieving an accuracy rate of 96.7%.

Supported by the New Brunswick Innovation Foundation.

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Acknowledgments

This research was enabled in part by support provided by the New Brunswick Innovation Foundation.

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Authors and Affiliations

  1. Perception, Robotics and Intelligent Machines (PRIME), Moncton University, Moncton, Canada

    Rebecca Kipanga Masele & Fadoua Khennou

Authors
  1. Rebecca Kipanga Masele
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  2. Fadoua Khennou
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Correspondence to Fadoua Khennou .

Editor information

Editors and Affiliations

  1. Kaunas University of Technology, Kaunas, Lithuania

    Audrius Lopata

  2. Kaunas University of Technology, Kaunas, Lithuania

    Daina Gudonienė

  3. Kaunas University of Technology, Kaunas, Lithuania

    Rita Butkienė

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Masele, R.K., Khennou, F. (2024). Android Malware Detection Using Artificial Intelligence. In: Lopata, A., Gudonienė, D., Butkienė, R. (eds) Information and Software Technologies. ICIST 2023. Communications in Computer and Information Science, vol 1979. Springer, Cham. https://doi.org/10.1007/978-3-031-48981-5_5

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  • DOI: https://doi.org/10.1007/978-3-031-48981-5_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48980-8

  • Online ISBN: 978-3-031-48981-5

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