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User Authentication: Keystroke Dynamics with Soft Biometric Features
Published in B.K. Tripathy, J. Anuradha, Internet of Things (IoT), 2017
Soumen Roy, Devadatta Sinha, Utpal Roy
Knowledge-based user authentication technique is a common and easy access control mechanism. But people are uninspired while choosing a healthy password or PIN. It increases the probability of guessing attacks. In this situation, to minimize these attacks, keystroke dynamics is a good choice; here users are not only identified by the password but their typing style is also accounted for. Keystroke dynamics is the method of analyzing typing pattern on a computer keyboard or touch screen and classifying the users based on their regular typing rhythm. It is a behavioral biometric characteristic which we have learned in our life and relates to the issues in human identification/authentication. This is the method where people can be identified by their typing style similar to hand writing or voice print. Being noninvasive and cost-effective, this method is a good field of research. But the performance of keystroke dynamics is less than other popular morphological biometric characteristics like face print, iris, and finger print recognition due to high rate of intraclass variation or high Failure to Enroll Rate (FER). So, this technique demands higher level of security. In this chapter, we are interested in investigating the integration of the soft biometric features, gender and age group, with the existing keystroke dynamics user authentication systems proposed by [1–3].
Sensor Networking Software and Architectures
Published in John R. Vacca, Handbook of Sensor Networking, 2015
Apart from the conventional biometrics like face, fingerprint, and iris, work is increasingly being done on other biometric traits, such as keystroke dynamics (for computers and other electronics) and ECG (for telemedicine). Keystroke dynamics is the process of analyzing the way a user types at a terminal by monitoring the keyboard inputs thousands of times per second and attempts to identify them based on habitual rhythm patterns in the way they type. According to some people, the use of keystroke rhythm is a natural choice for computer security. This argument stems from observations that similar neurophysiological factors that make written signatures unique are also exhibited in a user's typing pattern [15]. When a person types, the latencies between successive keystrokes, keystroke durations, finger placement, and applied pressure on the keys can be used to construct a unique signature (i.e., profile) for that individual. For well-known, regularly typed strings, such signatures can be quite consistent. Furthermore, recognition based on typing rhythm is not intrusive, making it quite applicable to computer access security, as users will be typing at the keyboard anyway. Moreover, unlike other biometric systems, which may be expensive to implement, keystroke dynamics is almost free-the only hardware required is the keyboard, which is attached to all computers.
Behavioral Biometrics: A Prognostic Measure for Activity Recognition
Published in Karm Veer Arya, Robin Singh Bhadoria, The Biometric Computing, 2019
Ankit A. Bhurane, Robin Singh Bhadoria
In this chapter, we have given an overview of popular behavioral biometrics. The keystroke dynamics is recommended to be used as auxiliary protection to the existing authentication instead of treating it as standalone biometric. A detailed evaluation of the performance can be found in work by Khalid Saeed with Marcin Adamski (2017). In the case of long text verification, the efficiency and time required for evaluation increases with an increase in the number of users. Further, it should be noted that the database needs to be regularly updated due to the dynamicity of the rhythm of the typists. However, the ease of data capture and processing makes the keystroke dynamics as one of the top choices in behavioral biometrics.
Performance Evaluation of Fingerprint Dynamics in Machine Learning and Score Level Fusion Framework
Published in IETE Technical Review, 2018
Ishan Bhardwaj, Narendra D. Londhe, Sunil K. Kopparapu
The fingerprint dynamics shares the same principle as keystroke dynamics. Keystroke dynamics is an acclaimed technique that analyses the user typing style by monitoring the keyboard inputs over a period of time. It exploits their habitual typing pattern for the purpose of user authentication [14]. Keyboard characteristics are considered rich in cognitive qualities [3]. Although behavioural biometric techniques are considered to perform not at par with physical traits like fingerprints [6]; many studies, such as by National Institute of Standard and Technology and National Science Foundation, claimed that keystroke patterns of an individual can be as unique as of physiological biometric traits [15]. Researchers (Leggett and Williams) [16] first engineered keystroke dynamics as a method to distinguish individuals. This was later followed by other researchers who suggested different dimensions based on typing phrases, login identification (ID) only, login ID and passwords, etc. [17,18]. Some researchers preferred inter-key or hold-times alone in keystroke dynamics while others found better performance using both, depending on the type of classifier or string length they have used. Some studies [14,18] also report that involving more than one feature can give better recognition results. Many researchers like Montalvão et al., and Kang and Cho [19,20] advocate the significance of string length in the keystroke-based authentication and report a drastic increase in misclassification rates when the string length drops below 10 characters. A number of good comparative studies and surveys of keystroke dynamics are available in the literature [17,21].
Continuous Authentications Using Frequent English Terms
Published in Applied Artificial Intelligence, 2018
Alaa Darabseh, Sima Siami-Namini, Akbar Siami Namin
Keystroke dynamics are defined as “a behavioral biometric characteristic, which involves analyzing a computer user’s habitual typing pattern when interacting with a computer keyboard” (Monrose and Rubin 2000). There are several advantages of using Keystroke Dynamics (Gunetti and Picardi 2005): First, keystroke dynamics are practical and feasible, since every computer user types on a keyboard; second, it is inexpensive due to the fact that it does not require any additional or special tools or components; thirdly, typing rhythms can be still available even after the authentication phase has passed.