In this paper we investigate the problem of user authentication using keystroke biometrics. A new distance metric that is effective in dealing with the challenges intrinsic to keystroke dynamics data, i.e., scale variations, feature interactions and redundancies, and outliers is proposed. Our keystroke biometrics algorithms based on this new distance metric are evaluated on the CMU keystroke dynamics benchmark dataset and are shown to be superior to algorithms using traditional distance metrics. With the ever increasing demand for more secure access control in many of today's security applications, traditional methods such as PINs, tokens, or passwords fail to keep up with the challenges presented because they can be lost or stolen, which compromises the system security. [26] provides a natural choice for secure "password-free" computer access. Keystroke dynamics refers to the habitual patterns or rhythms an individual exhibits while typing on a keyboard input device. These rhythms and patterns of tapping are idiosyncratic [5] , in the same way as handwritings or signatures, due to their similar governing neurophysiological mechanisms. As early as in the 19 th century, telegraph operators could recognize each other based on one's specific tapping style [18] . This suggests that keystroke dynamics contain sufficient information to serve as a potential biometric identifier to ascertain a specific keyboard user. Compared to other biometrics, keystroke biometrics has additional desirable properties due to its user-friendliness and non-intrusiveness. Keystroke dynamics data can be collected without a user's cooperation or even awareness. Continuous authentication is possible using keystroke dynamics just as a mere consequence of people's use of computers. Unlike many other biometrics, the temporal information of keystrokes can be collected to ascertain a user using only software and no additional hardware. In summary, keystroke dynamics biometrics enables a cost effective, user friendly, and continuous user authentication with potential for high accuracy. Although keystroke dynamics is governed by a person's neurophysiological pathway to be highly individualistic, it can also be influenced by his or her psychological state. As a "behavioral" biometrics [35] , keystroke dynamics exhibits instabilities due to transient factors such as emotions, stress, and drowsiness etc [6] . It also depends on external factors, such as the input keyboard device used, possibly due to different layout of the keys. The keying times can be noisy with outliers. As keystroke biometrics exploits the habitual rhythm in typing, it has been observed that keystrokes of frequently typed words or strings show more consistency and are better discerners [22] [38]. Keystroke biometrics can use "static text", where keystroke dynamics of a specific pre-enrolled text, such as a password, is analyzed at a certain time, e.g., during the log on process. For more secure applications, "free text" should be used to continuously authenticate a user. The rest of the paper is organized as follows. In section 2 we will review the current state of keystroke biometric techniques. We discuss the strength and limitations of two top performing distance metrics for keystroke dynamics and propose a new distance metric that combines the benefits of both these schemes in section 3. Section 4 describes our keystroke dynamics classifiers. Section 5 presents the experiments and performance study of the proposed algorithms. In section 6 we summarize our approach and outline future work. Of late, keystroke dynamics has become an active research area due to the increasing importance of cyber security and computer or network access control. Most of
Keystroke has become an active area of research because of the increasing requirement of security and access control in network or computer access REF .
Keystroke dynamics for user authentication
2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Computer Science