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Prediction of Android Malicious Software Using Boosting Algorithms

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Emerging Technologies in Computing (iCETiC 2021)

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Abstract

Android malware, a group of malicious software variants, including viruses, ransomware and spyware, designed to cause substantial damage to data and systems or to access a network without authorization. With an inexorable shift in technology, Android has supplanted other Mobile platforms by being flexible and user-friendly to the users. As the number of Android apps continues to grow every day, the number of malwares aimed at attacking those users is also on the rise. Thus, it becomes emergent to identify and remove malicious Android applications before installation to prevent user’s loss. Several studies have already been carried out to anticipate Android malware using machine learning algorithms, while as per the literature survey conducted by this study, a significant research has not been found to be focusing especially on the genre of boosting algorithms. Therefore, the objective of this paper is to classify malicious and benign Android applications by using Boosting algorithm. To attain the research objective, four widely defined boosting models viz. AdaBoost, CatBoost, XGBoost, and GradientBoost were developed whereas, it was found that CatBoost and GradientBoost had the highest F1 score (93.9%), followed by Adaboost (F1 score 93.5%), and XGBoost (F1 score 93.5%).

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Author information

Authors and Affiliations

  1. Department of Information and Communication Technology, Bangladesh University of Professionals (BUP), Dhaka, Bangladesh

    Deepon Deb Nath

  2. Department of Computer Science and Engineering, Military Institute of Science and Technology (MIST), Dhaka, Bangladesh

    Nafiz Imtiaz Khan

  3. Institute of Information Technology, Jahangirnagar University, Dhaka, Bangladesh

    Jesmin Akhter

  4. Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh

    Abu Sayed Md. Mostafizur Rahaman

Authors
  1. Deepon Deb Nath
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  2. Nafiz Imtiaz Khan
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  3. Jesmin Akhter
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  4. Abu Sayed Md. Mostafizur Rahaman
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Corresponding author

Correspondence to Nafiz Imtiaz Khan .

Editor information

Editors and Affiliations

  1. Xiamen University Malaysia, Sepang, Malaysia

    Mahdi H. Miraz

  2. University of Chester, Chester, UK

    Garfield Southall

  3. Epoka University, Tiranë, Albania

    Maaruf Ali

  4. University of South Wales, Pontypridd, Mid Glamorgan, UK

    Andrew Ware

  5. University of Fairfax, Fairfax, VA, USA

    Safeeullah Soomro

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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Nath, D.D., Khan, N.I., Akhter, J., Rahaman, A.S.M.M. (2021). Prediction of Android Malicious Software Using Boosting Algorithms. In: Miraz, M.H., Southall, G., Ali, M., Ware, A., Soomro, S. (eds) Emerging Technologies in Computing. iCETiC 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 395. Springer, Cham. https://doi.org/10.1007/978-3-030-90016-8_2

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  • DOI: https://doi.org/10.1007/978-3-030-90016-8_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90015-1

  • Online ISBN: 978-3-030-90016-8

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