A survey on touch dynamics authentication in mobile devices

Abstract

There have been research activities in the area of keystroke dynamics biometrics on physical keyboards (desktop computers or conventional mobile phones) undertaken in the past three decades. However, in terms of touch dynamics biometrics on virtual keyboards (modern touchscreen mobile devices), there has been little published work. Particularly, there is a lack of an extensive survey and evaluation of the methodologies adopted in the area. Owing to the widespread use of touchscreen mobile devices, it is necessary for us to examine the techniques and their effectiveness in the domain of touch dynamics biometrics. The aim of this paper is to provide some insights and comparative analysis of the current state-of-the-art in the topic area, including data acquisition protocols, feature data representations, decision making techniques, as well as experimental settings and evaluations. With such a survey, we can gain a better understanding of the current state-of-the-art, thus identifying challenging issues and knowledge gaps for further research.

On Designing a Questionnaire Based Honeyword Generation Approach for Achieving Flatness

“…We varied the value of q from 6 to 8 for both these cases (q s value was selected based on the users' performance shown in Table 1). It is important to note that according to the report in (Teh et al, 2016), most of the studies in this area used a test group between 11 and 25, while very few used a test group of 50 or more participants (Kambourakis et al, 2016). Therefore, we believe that a set of 80 participants provides a better insight about the proposed scheme.…”

Authentication and Authorization for Mobile IoT Devices Using Biofeatures: Recent Advances and Future Trends

“…Sundararajan and Woodard [16] surveyed different 100 approaches that leveraged deep learning and various biometric modalities to identify users. Teh et al [13] presented different authentication solutions that rely on touch dynamics in mobile devices. Rattani and Derakhshani [17] provided the state-ofthe-art related to face biometric authentication schemes that are designed for mobile devices.…”

“…• Touch dynamics: The process of measuring and assessing human touch rhythm on mobile IoT devices is called touch dynamics. According to Teh et al [13], the design of a touch dynamics authentication system is performed in three steps, namely, 1) User enrolment step, 2) User authentication step, and 3) Data retraining step, as shown in Fig. 4.…”

“…This amount is calculated without taking into account the economic problems and psychological oppression that victims of this fraud suffer. From the banking sector and businesses, to access to homes, cars, personal computers and mobile devices, biometric technology offers the highest level of security in Gafurov (2007) [4] Not mobile Gait recognition No No No Revett et al (2008) [5] Not mobile Mouse dynamics No No No Yampolskiy and Govindaraju (2008) [6] Not mobile Behavioral-based No No No Shanmugapriya and Padmavathi (2009) [7] Not mobile Keystroke dynamics No No Yes Karnan et al (2011) [8] Not mobile Keystroke dynamics No No Yes Banerjee and Woodard (2012) [9] Not mobile Keystroke dynamics No No Yes Teh et al (2013) [10] Not mobile Keystroke dynamics No No Yes Bhatt et al (2013) [11] Not mobile Keystroke dynamics No No Yes Meng et al (2015) [12] Mobile device All Yes Yes Partial Teh et al (2016) [13] Mobile device Touch dynamics No No Yes Mahfouz et al (2017) [14] Smartphone behavioral-based No No Yes Mahadi et al (2018) [15] Not mobile behavioral-based No No Yes Sundararajan and Woodard (2018) [16] Not mobile All No No Yes [17] Mobile device Face recognition Yes Yes Yes Our survey Mobile IoT device All Yes Yes Yes ML and DM: Machine learning (ML) and data mining (DM) algorithms terms of privacy and privacy protection and secure access.…”

Leveraging Battery Usage from Mobile Devices for Active Authentication

“…Several studies have investigated the concept of Active Authentication, using various different sources of information, like app usage [13], stylometry [13], keystroke dynamics [16-18, 27, 28], mouse movement [27], smart phone touch screen dynamics [29][30][31], phone calls placed [6,14], GPS location [6,13,14], ambient sound [20], and so forth. We refer to an extensive survey on behavioral biometrics [32] for a detailed comparison of accuracy rates when verifying users with different behavioral biometric approaches.…”