RHU Keystroke: A mobile-based benchmark for keystroke dynamics systems

Teoh

et al. 2016

Computers & Security

130

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.

Section: Subject Demographymentioning

confidence: 99%

Section: Acquisition Session and Intervalmentioning

confidence: 99%

Section: Public Datasetmentioning

confidence: 99%

See 1 more Smart Citation

A survey on touch dynamics authentication in mobile devices

Teoh

et al. 2016

Computers & Security

130

“…Our experiment involves more subjects, acquires less number of samples per subject, and uses a device with a larger screen size than many other studies. The acquired dataset is made publically available, and unlike other public datasets (Antal and Nemes 2016;El-Abed et al 2014), this dataset uses numerical-based input strings. 3.…”

Section: Related Workmentioning

confidence: 99%

Strengthen user authentication on mobile devices by using user’s touch dynamics pattern

J Ambient Intell Human Comput

Tan

et al. 2019

Mobile devices, particularly the touch screen mobile devices, are increasingly used to store and access private and sensitive data or services, and this has led to an increased demand for more secure and usable security services, one of which is user authentication. Currently, mobile device authentication services mainly use a knowledge-based method, e.g. a PIN-based authentication method, and, in some cases, a fingerprint-based authentication method is also supported. The knowledgebased method is vulnerable to impersonation attacks, while the fingerprint-based method can be unreliable sometimes. To overcome these limitations and to make the authentication service more secure and reliable for touch screen mobile device users, we have investigated the use of touch dynamics biometrics as a mobile device authentication solution by designing, implementing and evaluating a touch dynamics authentication method. This paper describes the design, implementation, and evaluation of this method, the acquisition of raw touch dynamics data, the use of the raw data to obtain touch dynamics features, and the training of the features to build an authentication model for user identity verification. The evaluation results show that by integrating the touch dynamics authentication method into the PIN-based authentication method, the protection levels against impersonation attacks is greatly enhanced. For example, if a PIN is compromised, the success rate of an impersonation attempt is drastically reduced from 100% (if only a 4-digit PIN is used) to 9.9% (if both the PIN and the touch dynamics are used).

“…The data collection process conducted by (El-Abed et al, 2014) involved 51 subjects. The input is a fixed password "rhu.university" entered on a virtual keyboard of a window touchscreen phone (Nokia Lumia 920).…”

Section: Public Datasetsmentioning

confidence: 99%

TDAS: a touch dynamics based multi-factor authentication solution for mobile devices

Teoh

et al. 2016

IJPCC

The use of mobile devices in handling our daily activities that involve the storage or access of sensitive data (e.g. on-line banking, paperless prescription services, etc.) is becoming very common. These mobile electronic services typically use a knowledge-based authentication method to authenticate a user (claimed identity). However, this authentication method is vulnerable to several security attacks. To counter the attacks and to make the authentication process more secure, this paper investigates the use of touch dynamics biometrics in conjunction with a PIN-based authentication method, and demonstrates its benefits in terms of strengthening the security of authentication services for mobile device. Design/methodology/approach The investigation has made use of three light-weighted matching functions and a comprehensive reference dataset collected from 150 subjects. Findings The investigative results show that, with this multi-factor authentication approach, even when the PIN is exposed, as much as 9 out of 10 impersonation attempts can be successfully identified. It has also been discovered that the accuracy performance can be increased by combining different feature data types and by increasing the input string length. Originality/value The novel contributions of this paper are twofold. Firstly, it describes how a comprehensive experiment is set up to collect touch dynamics biometrics data, and the set of collected data is being made publically available, which may facilitate further research in the problem domain. Secondly, the paper demonstrates how the dataset may be used to strengthen the protection of resources that are accessible via mobile devices.