Recent advances in mobile touch screen security authentication methods: A systematic literature review

Ibrahim, Tahir Musa; Abdulhamid, Shafií Muhammad; Alarood, Ala Abdulsalam; Chiroma, Haruna; Al-Garadi, Mohammed Ali; Rana, Nadim; Muhammad, Amina Nuhu; Ibrahim, Adamu Abubakar; Haruna, Khalid; Gabralla, Lubna Abdelkareim

doi:10.1016/j.cose.2019.04.008

Cited by 15 publications

(4 citation statements)

References 42 publications

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“…It has been observed from the performance metrics analysis as shown in Section 6 that different studies used different performance evaluation criteria depending on the study. Similar findings were reported in mobile phone authentication system [91], [92]. This signified that this research domain lacks uniform performance metrics in evaluating the efficacy of deep learning based smart device.…”

Section: Open Research Challenges and Future Directionsupporting

confidence: 80%

Detecting Elderly Behaviors Based on Deep Learning for Healthcare: Recent Advances, Methods, Real-World Applications and Challenges

2022

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Machine learning has been applied in healthcare domain for the development of smart devices to improve the life of the elderly persons in the society. Taking care of elderly person in the society is a critical issue that need automation. To proffer solution, many researchers developed deep learning algorithms smart devices for detecting elderly behavior to improve the elderly healthcare. Despite the progress made in the applications of deep learning algorithms in elderly healthcare systems, to the best of the author's knowledge no comprehensive recent development has been published on this interesting research area especially focusing on deep learning. In this paper, we presented a comprehensive recent development on the advances, methods and real world applications on developing smart devices for detecting elderly behavior for use in smart home, smart clinic, smart hospital and smart elderly nursing home for elderly person's healthcare. Theories of the deep learning algorithms, recent development recorded as regard to the applicability of deep learning in elderly healthcare systems and case studies were discussed. A taxonomy based on the data extracted from the applicability of deep learning algorithms in elderly healthcare systems is created to ease pointing out areas that need more attention. The article shows that the deep learning algorithm that received tremendous attention from researchers is convolutional neural network architecture and its variants. To help in future development of the research area, we highlighted the challenges associated to the applicability of deep learning algorithms in elderly healthcare system and pointed out new point of view for future research. The research community can use our review as a benchmark for proposing novel deep learning algorithms based smart devices to detect elderly behavior for elderly healthcare systems. Industries and organizations can use the paper as a guide in selecting machine learning based smart device for detecting elderly behavior for elderly healthcare support.INDEX TERMS Machine learning, deep learning algorithms, convolutional neural network, elderly person behaviour, VGG, Internet of Things, smart nursing home.

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Section: Open Research Challenges and Future Directionsupporting

confidence: 80%

Detecting Elderly Behaviors Based on Deep Learning for Healthcare: Recent Advances, Methods, Real-World Applications and Challenges

2022

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“…Though the purpose of this review is to examine the current scope of literature on behavioral biometrics through the lens of its application to public health (i.e., screen time measurement), it is necessary to also distinguish this from the domain of behavioral biometrics research for security. Given the vast amount of sensitive information stored on mobile devices, secure user authentication has become a prominent concern and a highly studied concept over recent years [ 22 , 160 , 161 ]. User authentication has shifted from “what you know,” such as an ID, PIN, or password, to “what you are,” or biometric authentication, with behavioral biometrics referring to the specific user-device interaction.…”

Section: Discussionmentioning

confidence: 99%

“…Modern mobile devices contain a variety of sensors (e.g., accelerometer, gyroscope, magnetometer, touch) that collect multiple data streams and can provide characteristic information about the user. These sensors provide the basis for behavioral biometric authentication [ 20 – 22 ]. Unlike physiological biometrics (e.g., fingerprint, iris, facial recognition), behavioral biometrics do not require additional hardware in modern mobile devices [ 23 , 24 ], making it a feasible research tactic for screen time measurement.…”

Section: Introductionmentioning

confidence: 99%

The utility of behavioral biometrics in user authentication and demographic characteristic detection: a scoping review

Finnegan,

White,

Armstrong

et al. 2024

Syst Rev

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Background Objective measures of screen time are necessary to better understand the complex relationship between screen time and health outcomes. However, current objective measures of screen time (e.g., passive sensing applications) are limited in identifying the user of the mobile device, a critical limitation in children’s screen time research where devices are often shared across a family. Behavioral biometrics, a technology that uses embedded sensors on modern mobile devices to continuously authenticate users, could be used to address this limitation. Objective The purpose of this scoping review was to summarize the current state of behavioral biometric authentication and synthesize these findings within the scope of applying behavioral biometric technology to screen time measurement. Methods We systematically searched five databases (Web of Science Core Collection, Inspec in Engineering Village, Applied Science & Technology Source, IEEE Xplore, PubMed), with the last search in September of 2022. Eligible studies were on the authentication of the user or the detection of demographic characteristics (age, gender) using built-in sensors on mobile devices (e.g., smartphone, tablet). Studies were required to use the following methods for authentication: motion behavior, touch, keystroke dynamics, and/or behavior profiling. We extracted study characteristics (sample size, age, gender), data collection methods, data stream, model evaluation metrics, and performance of models, and additionally performed a study quality assessment. Summary characteristics were tabulated and compiled in Excel. We synthesized the extracted information using a narrative approach. Results Of the 14,179 articles screened, 122 were included in this scoping review. Of the 122 included studies, the most highly used biometric methods were touch gestures (n = 76) and movement (n = 63), with 30 studies using keystroke dynamics and 6 studies using behavior profiling. Of the studies that reported age (47), most were performed exclusively in adult populations (n = 34). The overall study quality was low, with an average score of 5.5/14. Conclusion The field of behavioral biometrics is limited by the low overall quality of studies. Behavioral biometric technology has the potential to be used in a public health context to address the limitations of current measures of screen time; however, more rigorous research must be performed in child populations first. Systematic review registration The protocol has been pre-registered in the Open Science Framework database (https://doi.org/10.17605/OSF.IO/92YCT).

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“…The development of technology today focuses a lot on security for touch screen technology, which is often used on mobile devices [3]. However, after the widespread introduction of the mobile operating system a few years ago, many alternatives to device authentication emerged, such as the introduction of the Personal Identification Number (PIN), which is now widely used for information security, as well as other alternatives, such as biometrics, patterns, gestures, and graphical authentication [4,5].…”

Section: Introductionmentioning

confidence: 99%

Seamless Security on Mobile Devices Textual Password Quantification Model Based Usability Evaluation of Secure Rotary Entry Pad Authentication

Kabetta,

Setiawan,

Amelia

et al. 2023

matrik

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Mobile devices are vulnerable to shoulder surfing and smudge attacks, which should occur when a user enters a PIN for authentication purposes. This attack can be avoided by implementing a rotary entry pad mechanism. Despite this, several studies have found that using a rotary entry pad reduces user usability. This study uses a Design Research Methodology approach. It will implement a rotary entry pad authentication in the Android operating system as an authentication method to protect the device against Shoulder Surfing Attacks and Smudge Attacks. Furthermore, it combined JSON Web Token (JWT) to secure the authentication process from the client to the server. At the end of implementation, it compared with other studies in terms of usability and evaluated it using the TQ-Model, which showed that the usability aspect has improved. Regarding security, we conducted a shoulder surfing attack simulation to assess the efficacy of guessing PINs. The results showed that only a limited number of attempts were successful, with two out of five samples failing to guess any numbers and only one sample successfully guessing six 10-digit PIN combinations out of 10 to the power of 10. The security test results show that shoulder surfing attacks are more difficult to perform after implementing the rotary entry pad. The evaluation showed that the JSpinpad performed better, with seven parameters showing improvement, one parameter showing a decline, and ten parameters remaining unchanged.

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Recent advances in mobile touch screen security authentication methods: A systematic literature review

Cited by 15 publications

References 42 publications

Detecting Elderly Behaviors Based on Deep Learning for Healthcare: Recent Advances, Methods, Real-World Applications and Challenges

Detecting Elderly Behaviors Based on Deep Learning for Healthcare: Recent Advances, Methods, Real-World Applications and Challenges

The utility of behavioral biometrics in user authentication and demographic characteristic detection: a scoping review

Seamless Security on Mobile Devices Textual Password Quantification Model Based Usability Evaluation of Secure Rotary Entry Pad Authentication

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