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Analysis of Data Obtained from the Mobile Botnet

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Advances in Mobile Computing and Multimedia Intelligence (MoMM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14417))

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Abstract

As the use of mobile devices increases, the security risks associated with them also steadily increase. One of the most serious threats is the presence of mobile botnets, which are a group of devices controlled by cybercriminals to launch attacks or data theft. Identifying infected devices is a key step in counteracting these hazards. This article presents an analysis of the data collected in the experiment using a mobile botnet application. We focused on the analysis of the generated network traffic and events registered by mobile devices. As our results show, such data analysis and searching for patterns left by malicious software in today’s reality can no longer remain an efficient tool for the detection of such threats. The results highlight the need for further research and improvement of techniques for the detection of mobile botnet members to improve the efficiency and accuracy of their identification. This article also describes possible reasons for the lack of unambiguous results and presents proposals for further research.

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Acknowledgments

This work was supported by the Polish National Centre of Research and Development under the CyberSecIdent Programme within CYBERSECIDENT/489912/ IV/NCBR/2021 project.

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Authors and Affiliations

  1. Institute of Computer Science, University of Opole, Opole, Poland

    Jaroslaw Kobiela & Piotr Urbaniec

Authors
  1. Jaroslaw Kobiela
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  2. Piotr Urbaniec
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Correspondence to Jaroslaw Kobiela .

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Editors and Affiliations

  1. Monash University, Melbourne, VIC, Australia

    Pari Delir Haghighi

  2. Johannes Kepler University Linz, Linz, Austria

    Ismail Khalil

  3. Johannes Kepler University Linz, Linz, Austria

    Gabriele Kotsis

  4. Udayana University, Denpasar, Indonesia

    Ngurah Agus Sanjaya ER

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Kobiela, J., Urbaniec, P. (2023). Analysis of Data Obtained from the Mobile Botnet. In: Delir Haghighi, P., Khalil, I., Kotsis, G., ER, N.A.S. (eds) Advances in Mobile Computing and Multimedia Intelligence. MoMM 2023. Lecture Notes in Computer Science, vol 14417. Springer, Cham. https://doi.org/10.1007/978-3-031-48348-6_2

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  • DOI: https://doi.org/10.1007/978-3-031-48348-6_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48347-9

  • Online ISBN: 978-3-031-48348-6

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