Understanding Keystroke Dynamics for Smartphone Users Authentication and Keystroke Dynamics on Smartphones Built-In Motion Sensors

Lee, Hyungu; Hwang, Jung Yeon; Kim, Dong In; Lee, Shincheol; Lee, Sung-Hoon; Shin, Ji‐Sun

doi:10.1155/2018/2567463

Cited by 29 publications

(16 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides the traditional biometrics like the fingerprint and face, behavioral biometrics have also been used for user authentication of mobile devices. In [19], keystroke dynamics were combined with the PIN as multifactor authentication for the mobile user. Data from the motion sensors, such as the accelerometer, gyroscope, and rotation was included to improve the performance of authentication.…”

Section: Biometrics-based Mobile User Authenticationmentioning

confidence: 99%

Enhancing the Security of Pattern Unlock with Surface EMG-Based Biometrics

Dong

Zheng

2020

Applied Sciences

View full text Add to dashboard Cite

Pattern unlock is a popular screen unlock scheme that protects the sensitive data and information stored in mobile devices from unauthorized access. However, it is also susceptible to various attacks, including guessing attacks, shoulder surfing attacks, smudge attacks, and side-channel attacks, which can achieve a high success rate in breaking the patterns. In this paper, we propose a new two-factor screen unlock scheme that incorporates surface electromyography (sEMG)-based biometrics with patterns for user authentication. sEMG signals are unique biometric traits suitable for person identification, which can greatly improve the security of pattern unlock. During a screen unlock session, sEMG signals are recorded when the user draws the pattern on the device screen. Time-domain features extracted from the recorded sEMG signals are then used as the input of a one-class classifier to identify the user is legitimate or not. We conducted an experiment involving 10 subjects to test the effectiveness of the proposed scheme. It is shown that the adopted time-domain sEMG features and one-class classifiers achieve good authentication performance in terms of the F 1 score and Half of Total Error Rate (HTER). The results demonstrate that the proposed scheme is a promising solution to enhance the security of pattern unlock.

show abstract

Section: Biometrics-based Mobile User Authenticationmentioning

confidence: 99%

Enhancing the Security of Pattern Unlock with Surface EMG-Based Biometrics

Dong

Zheng

2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…N-graph [27] feature is also based on key timings which have been used by researchers. Key hold time (the period for which a key is pressed) also known as dwell time is another popular time-based feature used very rigorously in past studies [28], [29]. These features extracted from the keypress events are termed as keystroke or touch features.…”

Section: Literature Reviewmentioning

confidence: 99%

A Three-Step Authentication Model for Mobile Phone User Using Keystroke Dynamics

et al. 2020

View full text Add to dashboard Cite

The use of keystroke dynamics for user authentication has evolved over the years and has found its application in mobile phones. But the primary challenge with mobile phones is that they can be used in any position. Thus, it becomes critical to analyze the use of keystroke dynamics using the data collected in various typing positions. This research proposed a three-step authentication model that could be used to authenticate a user who is using the mobile in sitting, walking, and relaxing position. Furthermore, the mobile orientation (portrait and landscape) was considered while taking input from the user. Apart from using traditional keystroke features, accelerometer data were also combined for classification using Random Forest(RF) and K-Nearest Neighbour(KNN) classifiers. The three-step authentication method was able to authenticate a user with an EER of 2.9% for the relaxing landscape position. Finally, the model was optimized using Particle Swarm Optimization (PSO) to reduce the feature set and make the model more practical for mobile phones. Optimization helped to reduce the number of features from 55 to 17 and improved the EER to 2.2%. The research validated that relaxing and walking positions are the best positions to authenticate a user using keystroke dynamics.

show abstract

“…In a 2018 study, with more advanced smartphone technology, there was an attempt to classify users using the data measured by a motion sensor built into a smartphone [ 9 ]. These results were compared with the existing results obtained without using motion data.…”

Section: Related Workmentioning

confidence: 99%

Keystroke Dynamics-Based Authentication Using Unique Keypad

Choi

Lee

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

Authentication methods using personal identification number (PIN) and unlock patterns are widely used in smartphone user authentication. However, these authentication methods are vulnerable to shoulder-surfing attacks, and PIN authentication, in particular, is poor in terms of security because PINs are short in length with just four to six digits. A wide range of research is currently underway to examine various biometric authentication methods, for example, using the user’s face, fingerprint, or iris information. However, such authentication methods provide PIN-based authentication as a type of backup authentication to prepare for when the maximum set number of authentication failures is exceeded during the authentication process such that the security of biometric authentication equates to the security of PIN-based authentication. In order to overcome this limitation, research has been conducted on keystroke dynamics-based authentication, where users are classified by analyzing their typing patterns while they are entering their PIN. As a result, a wide range of methods for improving the ability to distinguish the normal user from abnormal ones have been proposed, using the typing patterns captured during the user’s PIN input. In this paper, we propose unique keypads that are assigned to and used by only normal users of smartphones to improve the user classification performance capabilities of existing keypads. The proposed keypads are formed by randomly generated numbers based on the Mersenne Twister algorithm. In an attempt to demonstrate the superior classification performance of the proposed unique keypad compared to existing keypads, all tests except for the keypad type were conducted under the same conditions in earlier work, including collection-related features and feature selection methods. Our experimental results show that when the filtering rates are 10%, 20%, 30%, 40%, and 50%, the corresponding equal error rates (EERs) for the proposed keypads are improved by 4.15%, 3.11%, 2.77%, 3.37% and 3.53% on average compared to the classification performance outcomes in earlier work.

show abstract

Understanding Keystroke Dynamics for Smartphone Users Authentication and Keystroke Dynamics on Smartphones Built-In Motion Sensors

Cited by 29 publications

References 20 publications

Enhancing the Security of Pattern Unlock with Surface EMG-Based Biometrics

Enhancing the Security of Pattern Unlock with Surface EMG-Based Biometrics

A Three-Step Authentication Model for Mobile Phone User Using Keystroke Dynamics

Keystroke Dynamics-Based Authentication Using Unique Keypad

Contact Info

Product

Resources

About