A Survey of Keystroke Dynamics Biometrics <s> Feature Extraction. <s> As information and network security is exposing to increasing evil threats significantly, authentication is now playing a more and more important role in security defense system. In this paper, a model that authenticates and identifies access to computer systems and networks by applying keystroke manner is put forward. In the model, legitimate user's typing manner such as durations of keystrokes and latencies between keystrokes are collected during training step, and several features are extracted from the keystroke data. Then user's manner template of keystroke is built by principal component analysis. While identifying, some principal component scores of user data are used to judge the user's validity. The procedure of authentication is invisible to user. The results of related experiment show that the model has a good discerning ability. <s> BIB001 </s> A Survey of Keystroke Dynamics Biometrics <s> Feature Extraction. <s> This document presents the application of biometrics to strength conventional passwords. We show methods to analyze and compare users' keystroke patterns to be able to verify their identity when entering the password into a computer system. The problem's difficulty arises from the fact that only a very limited amount data is available and that new users can come and others may leave the system meantime. That is we are not allowed to re-evaluate all users' data during the application. We show test results based on 2400 records collected from 25 users of different typing ability. <s> BIB002 </s> A Survey of Keystroke Dynamics Biometrics <s> Feature Extraction. <s> Keystroke dynamics is the process of identifying individual users on the basis of their typing rhythms, which are in turn derived from the timestamps of key-press and key-release events in the keyboard. Many researchers have explored this domain, with mixed results, but few have examined the relatively impoverished territory of digits only, particularly when restricted to using a single finger - which might come into play on an automated teller machine, a mobile phone, a digital telephone dial, or a digital electronic security keypad at a building entrance. In this work, 28 users typed the same 10-digit number, using only the right-hand index finger. Employing statistical machine-learning techniques (random forest), we achieved an unweighted correct-detection rate of 99.97% with a corresponding false-alarm rate of 1.51%, using practiced 2-of-3 encore typing with outlier handling. This level of accuracy approaches sufficiency for two-factor authentication for passwords or PIN numbers. <s> BIB003 </s> A Survey of Keystroke Dynamics Biometrics <s> Feature Extraction. <s> Continuous user authentication with keystroke dynamics uses characters sequences as features. Since users can type characters in any order, it is imperative to find character sequences (n-graphs) that are representative of user typing behavior. The contemporary feature selection approaches do not guarantee selecting frequently-typed features which may cause less accurate statistical user-representation. Furthermore, the selected features do not inherently reflect user typing behavior. We propose four statistical-based feature selection techniques that mitigate limitations of existing approaches. The first technique selects the most frequently occurring features. The other three consider different user typing behaviors by selecting: n-graphs that are typed quickly; n-graphs that are typed with consistent time; and n-graphs that have large time variance among users. We use Gunetti's keystroke dataset and k-means clustering algorithm for our experiments. The results show that among the proposed techniques, the most-frequent feature selection technique can effectively find user-representative features. We further substantiate our results by comparing the most-frequent feature selection technique with three existing approaches (popular Italian words, common n-graphs, and least frequent n-graphs). We find that it performs better than the existing approaches after selecting a certain number of most-frequent n-graphs. <s> BIB004
Raw keystroke data are then processed and stored as reference template for future usage. Some preprocessing procedures may be applied before feature extraction to ensure or to increase the quality of feature data. These steps may include feature selection BIB004 , dimension reduction BIB001 , and outlier detection BIB003  BIB002 .