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Machine learning algorithms for improving security on touch screen devices: a survey, challenges and new perspectives

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Abstract

Mobile phone touch screen devices are equipped with high processing power and high memory. This led to users not only storing photos or videos but stored sensitive application such as banking applications. As a result of that the security system of the mobile phone touch screen devices becomes sacrosanct. The application of machine learning algorithms in enhancing security on mobile phone touch screen devices is gaining a tremendous popularity in both academia and the industry. However, notwithstanding the growing popularity, up to date no comprehensive survey has been conducted on machine learning algorithms solutions to improve the security of mobile phone touch screen devices. This survey aims to connect this gap by conducting a comprehensive survey on the solutions of machine learning algorithms to improve the security of mobile phone touch screen devices including the analysis and synthesis of the algorithms and methodologies provided for those solutions. This article presents a comprehensive survey and a new taxonomy of the state-of-the-art literature on machine learning algorithms in improving the security of mobile phone touch screen devices. The limitation of the methodology in each article reviewed is pointed out. Challenges of the existing approaches and new perspective of future research directions for developing more accurate and robust solutions to mobile phone touch screen security are discussed. In particular, the survey found that exploring of different aspects of deep learning solutions to improve the security of mobile phone touch screen devices is under-explored.

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Acknowledgement

This research is supported by TETFund Institutional Based Research Grant through Federal College of Education (Technical), Gombe, Nigeria.

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Authors and Affiliations

  1. Department of Mathematical Sciences, Abubakar Tafawa Balewa University, Bauchi, Nigeria

    Auwal Ahmed Bello & Abdulsalam Ya’u Gital

  2. Department of Computer Science, Federal College of Education (Technical), Gombe, Gombe, Nigeria

    Haruna Chiroma

  3. Department of Computer Science and Information Technology, Community College, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia

    Lubna A. Gabralla

  4. Department of Cyber Security Science, Federal University of Technology, Minna, Minna, Nigeria

    Shafi’i M. Abdulhamid

  5. Department of Information System, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Liyana Shuib

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  1. Auwal Ahmed Bello
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  2. Haruna Chiroma
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  3. Abdulsalam Ya’u Gital
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  4. Lubna A. Gabralla
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Correspondence to Shafi’i M. Abdulhamid.

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Bello, A.A., Chiroma, H., Gital, A.Y. et al. Machine learning algorithms for improving security on touch screen devices: a survey, challenges and new perspectives. Neural Comput & Applic 32, 13651–13678 (2020). https://doi.org/10.1007/s00521-020-04775-0

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