We investigate whether a classifier can continuously authenticate users based on the way they interact with the touchscreen of a smart phone. We propose a set of 30 behavioral touch features that can be extracted from raw touchscreen logs and demonstrate that different users populate distinct subspaces of this feature space. In a systematic experiment designed to test how this behavioral pattern exhibits consistency over time, we collected touch data from users interacting with a smart phone using basic navigation maneuvers, i.e., up-down and left-right scrolling. We propose a classification framework that learns the touch behavior of a user during an enrollment phase and is able to accept or reject the current user by monitoring interaction with the touch screen. The classifier achieves a median equal error rate of 0 for intrasession authentication, 2 -3 for intersession authentication, and below 4 when the authentication test was carried out one week after the enrollment phase. While our experimental findings disqualify this method as a standalone authentication mechanism for long-term authentication, it could be implemented as a means to extend screen-lock time or as a part of a multimodal biometric authentication system.
@cite_23: The variables that help make a handwritten signature a unique human identifier also provide a unique digital signature in the form of a stream of latency periods between keystrokes. This article describes a method of verifying the identity of a user based on such a digital signature, and reports results from trial usage of the system.
@cite_10: This chapter deals with the applications of keystroke dynamics to authenticate verify access to computer systems and networks. It presents our novel contribution to this area along with other related works. The use of computer systems and networks has spread at a rate completely unexpected a decade ago. Computer systems and network are being used in almost every aspect of our daily life. As a result, the security threats to computers and networks have also increased significantly. We give a background information including the goals of any security system for computers and networks, followed by types of security attacks on computers and networks. We present the applications of keystroke dynamics using interkey times and hold times as features to authenticate access to computer systems and networks.
@cite_20: 
@cite_2: We present a novel approach to improving the security of passwords. In our approach, the legitimate user's typing patterns (e.g., durations of keystrokes, and latencies between keystrokes) are combined with the user's password to generate a hardened password that is convincingly more secure than conventional passwords against both online and offline attackers. In addition, our scheme automatically adapts to gradual changes in a user's typing patterns while maintaining the same hardened password across multiple logins, for use in file encryption or other applications requiring a longterm secret key. Using empirical data and a prototype implementation of our scheme, we give evidence that our approach is viable in practice, in terms of ease of use, improved security, and performance
Early behavioral biometrics have been based on keystroke dynamics and mouse movements. In @cite_20 , the authors introduce a keyboard system that captures timing and pressure characteristics to identify users based on entering telephone numbers and PIN inputs. The work in @cite_10 improved the original system along different dimensions. The average error rates vary between 5 dynamics gained lots of popularity through @cite_2 , where it was used to augment password authentication with additional security. A survey on the large body of literature on authentication with keystroke dynamics is given in @cite_23 .