MineAuth: Mining Behavioural Habits for Continuous Authentication on a Smartphone

Pang, Xiaojian; Yang, Li; Liu, Maozhen; Ma, Jianfeng

doi:10.1007/978-3-030-21548-4_29

Cited by 3 publications

(2 citation statements)

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“…As can be seen in Table 7, while [11] proposed using a user's daily interactions with their smartphone in conjunction with the values of keystroke dynamics, this approach has the disadvantage of requiring an always-executable CPU, which can result in power consumption on mobile devices if the owner uses the device for the authentication process at all times. Moreover, the technique described in [19] is based on multi-facial biometrics but requires the use of auxiliary hardware such as global positioning systems, accelerometers, gyroscopes, magnetometers, linear accelerometers, gravity modalities, and rotation modalities.…”

Section: Computational Cost-basedmentioning

confidence: 99%

“…Security control research shows that the quality of biometric protection is fairly high. For example, keystroke features and sensors on a smartphone can achieve 97.90% accuracy [8], gain data from smartphones can be utilized to provide an accuracy of 98.79% [9], the average recognition of touch gestures based on interactions with phones is 74.97% [10], 98.30% accuracy can be reached based on a user's daily behavior on a mobile device [11], and using the unique keypad on a smartphone to enter PINs, the Equal Error Rate (EER) is 10.01% according to the keystroke dynamics [12]. Furthermore, earlier research has mainly focused on static keystrokes, analyzing users' fixed-text typing habits.…”

Section: Introductionmentioning

confidence: 99%

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Extraction of Hidden Authentication Factors from Possessive Information

Nanglae

Yakubu

Bhattarakosol

2023

JSAN

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Smartphones have emerged as a ubiquitous personal gadget that serve as a repository for individuals’ significant personal data. Consequently, both physiological and behavioral traits, which are classified as biometric technologies, are used in authentication systems in order to safeguard data saved on smartphones from unauthorized access. Numerous authentication techniques have been developed; however, several authentication variables exhibit instability in the face of external influences or physical impairments. The potential failure of the authentication system might be attributed to several unpredictable circumstances. This research suggests that the use of distinctive and consistent elements over an individual’s lifespan may be employed to develop an authentication classification model. This model would be based on prevalent personal behavioral biometrics and could be readily implemented in security authentication systems. The biological biometrics acquired from an individual’s typing abilities during data entry include their name, surname, email, and phone number. Therefore, it is possible to establish and use a biometrics-based security system that can be sustained and employed during an individual’s lifetime without the explicit dependance on the functionality of the smartphone devices. The experimental findings demonstrate that the use of a mobile touchscreen as the foundation for the proposed verification mechanism has promise as a high-precision authentication solution.

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Section: Computational Cost-basedmentioning

confidence: 99%