Authenticating User Using Keystroke Dynamics and Finger Pressure

Saevanee, Hataichanok; Bhattarakosol, Pattarasinee

doi:10.1109/ccnc.2009.4784783

Cited by 93 publications

(52 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They reported a 1% EER using a kNN algorithm and later obtained similar results using neural networks [48]. More recent studies on real mobile devices seem to suggest that pressure has a much smaller accuracy impact in practice, ultimately resulting in a 8.4% EER when combined with keystroke timings [55].…”

Section: Keystroke Dynamics On Software Keyboards For Mobile Devicessupporting

confidence: 52%

I Sensed It Was You: Authenticating Mobile Users with Sensor-Enhanced Keystroke Dynamics

Giuffrida¹,

Majdanik²,

Conti

et al. 2014

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Mobile devices have become an important part of our everyday life, harvesting more and more confidential user information. Their portable nature and the great exposure to security attacks, however, call out for stronger authentication mechanisms than simple password-based identification. Biometric authentication techniques have shown potential in this context. Unfortunately, prior approaches are either excessively prone to forgery or have too low accuracy to foster widespread adoption.In this paper, we propose sensor-enhanced keystroke dynamics, a new biometric mechanism to authenticate users typing on mobile devices. The key idea is to characterize the typing behavior of the user via unique sensor features and rely on standard machine learning techniques to perform user authentication. To demonstrate the effectiveness of our approach, we implemented an Android prototype system termed Unagi. Our implementation supports several feature extraction and detection algorithms for evaluation and comparison purposes. Experimental results demonstrate that sensor-enhanced keystroke dynamics can improve the accuracy of recent gestured-based authentication mechanisms (i.e., EER > 0.5%) by one order of magnitude, and the accuracy of traditional keystroke dynamics (i.e., EER > 7%) by two orders of magnitude.

show abstract

Section: Keystroke Dynamics On Software Keyboards For Mobile Devicessupporting

confidence: 52%

I Sensed It Was You: Authenticating Mobile Users with Sensor-Enhanced Keystroke Dynamics

Giuffrida¹,

Majdanik²,

Conti

et al. 2014

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…In 2009, Saevanee and Bhattarakosol [24] suggested keystroke dynamics using finger pressure on the touchscreen first. It showed 99% accuracy with the Probabilistic Neural Network (PNN).…”

Section: Related Workmentioning

confidence: 99%

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

Lee

Hwang

Kim

et al. 2018

Security and Communication Networks

View full text Add to dashboard Cite

Personal Identification Numbers (PINs) and pattern drawing have been used as common authentication methods especially on smartphones. Such methods, however, are very vulnerable to the shoulder surfing attack. Thus, keystroke dynamics that authenticate legitimate users based on their typing manner have been studied for years. However, many of the studies have focused on PC keyboard keystrokes. More studies on mobile and smartphones keystroke dynamics are warranted; as smartphones make progress in both hardware and software, features from smartphones have been diversified. In this paper, using various features including keystroke data such as time interval and motion data such as accelerometers and rotation values, we evaluate features with motion data and without motion data. We also compare 5 formulas for motion data, respectively. We also demonstrate that opposite gender match between a legitimate user and impostors has influence on authenticating by our experiment results.

show abstract

“…The study of keystroke dynamics was initially conducted on PCs and hardware keyboard (Bergadano et al 2002;Joyce and Gupta 1990;Kang et al 2007;Killourhy and Maxion 2010;Killourhy and Maxion 2009;Kotani and Horii 2005;Monrose and Rubin 1997;Monrose and Rubin 2000;Obaidat and Sadoun 1997;Peacock et al 2004). As mobile devices became increasingly popular, the research of keystroke dynamics switched to mobile devices (Campisi et al 2009;Clarke et al 2003;Hwang et al 2009;Karatzouni and Clarke 2007;Zahid et al 2009), and software keyboards (Huang et al 2012;Saevanee and Bhatarakosol 2008;Saevanee and Bhattarakosol 2009;Tasia et al 2014;Trojahn and Ortmeier 2012). Commercial products based on keystroke biometrics have been developed over the years (Id control 2018; Intensity analytics 2018; Keyboard biometrics -KeyTrac 2018; Plurilock Security Solutions Inc 2018).…”

Section: Keystroke Dynamicsmentioning

confidence: 99%

Making a good thing better: enhancing password/PIN-based user authentication with smartwatch

Chang

Wang

et al. 2018

Cybersecur

View full text Add to dashboard Cite

Wearing smartwatches becomes increasingly popular in people's lives. This paper shows that a smartwatch can help its bearer authenticate to a login system effectively and securely even if the bearer's password has already been revealed. This idea is motivated by our observation that a sensor-rich smartwatch is capable of tracking the wrist motions of its bearer typing a password or PIN, which can be used as an authentication factor. The major challenge in this research is that a sophisticated attacker may imitate a user's typing behavior as shown in previous research on keystroke dynamics based user authentication. We address this challenge by applying a set of machine learning and deep learning classifiers on the user's wrist motion data that are collected from a smartwatch worn by the user when inputting his/her password or PIN. Our solution is user-friendly since it does not require users to perform any additional actions when typing passwords or PINs other than wearing smartwatches. We conduct a user study involving 51 participants so as to evaluate the feasibility and performance of our solution. User study results show that the best classifier is the Bagged Decision Trees, which yields 4.58% FRR and 0.12% FAR on a QWERTY keyboard, and 6.13% FRR and 0.16% FAR on a numeric keypad.

show abstract

Authenticating User Using Keystroke Dynamics and Finger Pressure

Cited by 93 publications

References 1 publication

I Sensed It Was You: Authenticating Mobile Users with Sensor-Enhanced Keystroke Dynamics

I Sensed It Was You: Authenticating Mobile Users with Sensor-Enhanced Keystroke Dynamics

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

Making a good thing better: enhancing password/PIN-based user authentication with smartwatch

Contact Info

Product

Resources

About