Machine learning algorithms for improving security on touch screen devices: a survey, challenges and new perspectives
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ORIGINAL ARTICLE
Machine learning algorithms for improving security on touch screen devices: a survey, challenges and new perspectives Auwal Ahmed Bello1 • Haruna Chiroma2 • Abdulsalam Ya’u Gital1 • Lubna A. Gabralla3 • Shafi’i M. Abdulhamid4 Liyana Shuib5
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Received: 21 November 2018 / Accepted: 5 February 2020 Springer-Verlag London Ltd., part of Springer Nature 2020
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. Keywords Machine learning algorithms Deep learning Mobile phone touch screen Android Support vector machine Command attention Security
1 Introduction Mobile phones started as a basic cellular device where calls are made and limited text messages. Currently, touch screen mobile phone devices have revolutionized the & Shafi’i M. Abdulhamid [email protected]
mobile phone market as well as dominated the user-input technologies for the mobile phone devices. The revolution and dominance of the mobile phone touch screen devices are attributed to their high level of flexibility and good usability [1]. The use of mobile phone touch screen devices has become part of people lives including people with 1
Department of Mathematical Sciences, Abubakar Tafawa Balewa University, Bauchi, Nigeria
Auwal Ahmed Bello [email protected]
2
Department of Computer Science, Federal College of Education (Technical), Gombe, Gombe, Nigeria
Haruna Chiroma [email protected]; [email protected].
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