A Survey of Keystroke Dynamics Biometrics <s> Mobile Devices. <s> Smart phones are now being used to store users' identities and sensitive information/data. Therefore, it is important to authenticate legitimate users of a smart phone and to block imposters. In this paper, we demonstrate that keystroke dynamics of a smart phone user can be translated into a viable features' set for accurate user identification. To this end, we collect and analyze keystroke data of 25 diverse smart phone users. Based on this analysis, we select six distinguishing keystroke features that can be used for user identification. We show that these keystroke features for different users are diffused and therefore a fuzzy classifier is well-suited to cluster and classify them. We then optimize the front-end fuzzy classifier using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) as back-end dynamic optimizers to adapt to variations in usage patterns. Finally, we provide a novel keystroke dynamics based PIN (Personal Identification Number) verification mode to ensure information security on smart phones. The results of our experiments show that the proposed user identification system has an average error rate of 2% after the detection mode and the error rate of rejecting legitimate users drops to zero in the PIN verification mode. We also compare error rates (in terms of detecting both legitimate users and imposters) of our proposed classifier with 5 existing state-of-the-art techniques for user identification on desktop computers. Our results show that the proposed technique consistently and considerably outperforms existing schemes. <s> BIB001 </s> A Survey of Keystroke Dynamics Biometrics <s> Mobile Devices. <s> With the ever-increasing functionality and services accessible via mobile telephones, there is a strong argument that the level of user authentication implemented on the devices should be extended beyond the Personal Identification Number (PIN) that has traditionally been used. This paper presents the results of a survey of 297 mobile subscribers, which attempted to assess their use of mobile devices, their use of current authentication methods, and their attitudes towards future security options. The findings revealed that the majority of the respondents make significant use of their devices, with clear demands for protection against unauthorised use. However, the use of current PIN-based authentication is problematic, with a third of the respondents indicating that they do not use it at all, and other problems being reported amongst those that do. In view of this, the respondents' opinions in relation to future security options are interesting, with 83% being willing to accept some form of biometric authentication on their device. The discussion considers these findings, and the potential applicability of the preferred techniques to mobile devices. <s> BIB002 </s> A Survey of Keystroke Dynamics Biometrics <s> Mobile Devices. <s> A new approach for keystroke-based authentication when using a cellular phone keypad as input device is presented. In the proposed method, users are authenticated using keystroke dynamics acquired when typing fixed alphabetic strings on a mobile phone keypad. The employed statistical classifier is able to perform user verification with an average equal error rate of about 13%. The obtained experimental results suggest that, when using mobile devices, a strong secure authentication scheme cannot rely on the sole keystroke dynamics, which however can be a module of a more complex system including, as basic security, a password-based protocol eventually hardened by keystroke analysis. <s> BIB003 </s> A Survey of Keystroke Dynamics Biometrics <s> Mobile Devices. <s> Recently, mobile devices are used in financial applications such as banking and stock trading. However, unlike desktops and notebook computers, a 4-digit personal identification number (PIN) is often adopted as the only security mechanism for mobile devices. Because of their limited length, PINs are vulnerable to shoulder surfing and systematic trial-and-error attacks. This paper reports the effectiveness of user authentication using keystroke dynamics-based authentication (KDA) on mobile devices. We found that a KDA system can be effective for mobile devices in terms of authentication accuracy. Use of artificial rhythms leads to even better authentication performance. <s> BIB004 </s> A Survey of Keystroke Dynamics Biometrics <s> Mobile Devices. <s> Smartphones are increasingly being used to store personal information as well as to access sensitive data from the Internet and the cloud. Establishment of the identity of a user requesting information from smartphones is a prerequisite for secure systems in such scenarios. In the past, keystroke-based user identification has been successfully deployed on production-level mobile devices to mitigate the risks associated with naive username/password based authentication. However, these approaches have two major limitations: they are not applicable to services where authentication occurs outside the domain of the mobile device--such as web-based services; and they often overly tax the limited computational capabilities of mobile devices. In this paper, we propose a protocol for keystroke dynamics analysis which allows web-based applications to make use of remote attestation and delegated keystroke analysis. The end result is an efficient keystroke-based user identification mechanism that strengthens traditional password protected services while mitigating the risks of user profiling by collaborating malicious web services. We present a prototype implementation of our protocol using the popular Android operating system for smartphones. <s> BIB005
While typographical input from computer keyboard has been the main focus at the infancy stage of keystroke dynamics research, numerical base input from portable communicational devices has gradually gained attention since the wide spread use of cellular phone globally in the 20th century BIB002 . Research works such as BIB003 BIB004 performed experiments on conventional numerical key pad cellular phone in attempt to authenticate user via short input text. The initiative was encouraging but the issue of cross-platform compatibility across diverse model of devices remains an open question. Along with the rapid evolution of technology, mobile devices have also gained greater processing capability. Java enabled Symbian phone was selected by BIB001 as the platform for their study. They attempted to use several computational expensive neural network algorithms for recognition and have yielded some encouraging results. Unfortunately, a major setback was the degradation of response time to the mobile device that might affect user acceptance. A more recent publication reported by BIB005 used early generation smart phone with touch sensitive screen, which could be interacted via finger or stylus (special pointing stick). The trend of applying keystroke dynamics biometrics to newer hardware technology should be encouraged, since the interaction method, processing capability, and availability of these devices open to new research dimension and opportunity.