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

Teh, Pin Shen; Zhang, Ning; Teoh, Andrew Beng Jin; Chen, Ke

doi:10.1108/ijpcc-01-2016-0005

Cited by 20 publications

(15 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…5, by inputting one key, two time-data values are collected at down and up. With this collected data, we can calculate DT feature [48], [49] which denotes the duration user touches the screen; by subtracting time-down which represents the time occurred down from time-up which represents the time occurred up. The formula in Fig.…”

Section: ) Timementioning

confidence: 99%

A New Feature Scoring Method in Keystroke Dynamics-Based User Authentications

2020

View full text Add to dashboard Cite

In recent years, computing devices have become widely distributed, and the accumulated data from these devices are growing rapidly, especially as they are increasingly equipped with various sensors and RF communication capabilities. Data science, including machine learning technology, has contributed to the better handling the large amounts of data and feature selection techniques have been a useful strategy. As data amounts continue to grow, scaling features will became crucial in data science. In this paper, we propose a novel filter-based feature-selection method in the context of keystroke dynamics authentication. In particular, we propose a new feature-scoring method and apply it to keystroke-dynamics-based authentications. We implement keystroke-dynamics-based authentications multi-factored with PIN-based authentications and collect data from actual users' testing experiments. Then, we apply our feature-selection method and compare the performance with that when using all of the features and existing feature-selection methods. Our experimental results show that the classification performance by the proposed method is superior to those of the other methods by up to 21.8%. Moreover, our method provides security to other users' data sets, as the method utilizes only mean values from imposter data. Our feature-selection method contributes to improving the quality of keystroke dynamics authentications without user privacy issues. More generally, our method can also be applied to other data-mining data sets, such as IoT sensor data sets.

show abstract

Section: ) Timementioning

confidence: 99%

A New Feature Scoring Method in Keystroke Dynamics-Based User Authentications

2020

View full text Add to dashboard Cite

show abstract

“…Teh et al [75] – ‘TDAS: a touch dynamics based multi‐factor authentication solution for mobile devices’ . In this paper, the authors propose to implement the use of touch dynamics into a PIN‐based authentication system.…”

Section: Papers In Detailmentioning

confidence: 99%

Data behind mobile behavioural biometrics – a survey

2020

View full text Add to dashboard Cite

show abstract

“…The paper (Teh et al 2016b) reported an experiment carried out to collect a touch dynamics dataset from a group of subjects with a wider age range than those described above. In their experiments, five timing feature and a spatial feature were extracted from the input samples of the dataset, and, for each extracted feature, a reference template is created.…”

Section: Related Workmentioning

confidence: 99%

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

Teh

Zhang

Tan

et al. 2019

J Ambient Intell Human Comput

Self Cite

View full text Add to dashboard Cite

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).

show abstract

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

Cited by 20 publications

References 33 publications

A New Feature Scoring Method in Keystroke Dynamics-Based User Authentications

A New Feature Scoring Method in Keystroke Dynamics-Based User Authentications

Data behind mobile behavioural biometrics – a survey

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

Contact Info

Product

Resources

About