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

• Published in: Neural computing & applications 2020-09, Vol.32 (17), p.13651-13678
• Main Authors: Bello, Auwal Ahmed, Chiroma, Haruna, Gital, Abdulsalam Ya’u, Gabralla, Lubna A., Abdulhamid, Shafi’i M., Shuib, Liyana
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Cell phones; Cellular telephones; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Electronic devices; Image Processing and Computer Vision; Interactive computer systems; Machine learning; Original Article; Probability and Statistics in Computer Science; Security systems; Taxonomy; Touch screens
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-020-04775-0

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.