From keyloggers to touchloggers: Take the rough with the smooth

Damopoulos, Dimitrios; Kambourakis, Georgios; Gritzalis, Stefanos

doi:10.1016/j.cose.2012.10.002

Cited by 42 publications

(17 citation statements)

References 14 publications

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“…However, the 10 minutes session length is a serious limitation of this authentication scheme, as a phone should detect an intruder in a much shorter time. Damopoulos et al (2013) implemented a touchlogger for iOS based smartphones to demonstrate the ability of touch data for user identification. This paper presents touch data collected through natural usage of smartphones.…”

Section: Touch Usage Based User Profilingmentioning

confidence: 99%

Information revealed from scrolling interactions on mobile devices

Antal

Bokor

Szabó

2015

Pattern Recognition Letters

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Section: Touch Usage Based User Profilingmentioning

confidence: 99%

Information revealed from scrolling interactions on mobile devices

Antal

Bokor

Szabó

2015

Pattern Recognition Letters

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“…In the following, we survey the most relevant techniques in the area and refer the interested reader to more complete surveys [17].…”

Section: Related Workmentioning

confidence: 99%

“…Damopoulos et al [17] proposed a continuous authentication system using only touchscreen gestures. The authors reported a low 1% EER using predetermined touch patterns.…”

Section: Gesture-based Authenticationmentioning

confidence: 99%

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

Giuffrida¹,

Majdanik²,

Conti

et al. 2014

Lecture Notes in Computer Science

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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.

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“…location-based services, Internet sharing via tethering, and intelligent voice assistance to name just a few. In addition to the traditional cyber attacks and malware threats that plague legacy computers, smartphones now represent a promising target for malware developers that struggle to expose users sensitive data, compromise the device or manipulate popular services (Damopoulos et al, 2011(Damopoulos et al, , 2012a(Damopoulos et al, , 2013. Additionally, the number of stolen or lost smartphones has increased rapidly over the last few years.…”

Section: Introductionmentioning

confidence: 99%