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Title: Machine Learning Algorithms for Improving Security on Touch Screen Devices: A Survey, Challenges and New Perspectives
Authors: Bello, Auwal Ahmed
Chiroma, Haruna
Gital, Abdulsalam Y.
Gabralla, Lubna A.
Abdulhamid, Shafi’i Muhammad
Shuib, Liyana
Keywords: Deep learning
Mobile phone touch screen
Command attention
Support vector machine
Machine learning algorithms
Issue Date: 20-Jun-2020
Publisher: Neural Computing and Applications
Series/Report no.: 13651–13678;
Abstract: Mobilephonetouchscreendevicesareequippedwithhighprocessingpowerandhighmemory.Thisledtousersnotonlystoringphotos or videos but stored sensitive application such as banking applications. As a result of that the security system of themobile phone touch screen devices becomes sacrosanct. The application of machine learning algorithms in enhancing securityon 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 algo-rithms solutions to improve the security of mobile phone touch screen devices. This survey aims to connect this gap byconducting a comprehensive survey on the solutions of machine learning algorithms to improve the security of mobile phonetouchscreendevicesincludingtheanalysisandsynthesisofthealgorithmsandmethodologiesprovidedforthosesolutions.Thisarticle presents a comprehensive survey and a new taxonomy of the state-of-the-art literature on machine learning algorithms inimproving the security of mobile phone touch screen devices. The limitation of the methodology in each article reviewed ispointed out. Challenges of the existing approaches and new perspective of future research directions for developing moreaccurate and robust solutions to mobile phone touch screen security are discussed. In particular, the survey found that exploringof different aspects of deep learning solutions to improve the security of mobile phone touch screen devices is under-explored.
ISSN: 0941-0643
Appears in Collections:Cyber Security Science

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