KEYSTROKE DYNAMICS ANALYSIS USING MACHINE LEARNING METHODS

The primary objective of the paper was to determine the user based on its keystroke dynamics using the methods of machine learning. Such kind of a problem can be formulated as a classification task. To solve this task, four methods of supervised machine learning were employed, namely, logistic regression, support vector machines, random forest, and neural network. Each of three users typed the same word that had 7 symbols 600 times. The row of the dataset consists of 7 values that are the time period during which the particular key was pressed. The ground truth values are the user id. Before the application of machine learning classification methods, the features were transformed to z-score. The classification metrics were obtained for each applied method. The following parameters were determined: precision, recall, f1-score, support, prediction, and area under the receiver operating characteristic curve (AUC). The obtained AUC score was quite high. The lowest AUC score equal to 0.928 was achieved in the case of linear regression classifier. The highest AUC score was in the case of neural network classifier. The method of support vector machines and random forest showed slightly lower results as compared with neural network method. The same pattern is true for precision, recall and F1-score. Nevertheless, the obtained classification metrics are quite high in every case. Therefore, the methods of machine learning can be efficiently used to classify the user based on keystroke patterns. The most recommended method to solve such kind of a problem is neural network

Improving authentication accuracy using artificial rhythms and cues for keystroke dynamics-based authentication

Keystroke dynamics-based authentication (KDA) is to verify a user''''''''s identity using not only the password but also keystroke dynamics. With a small number of patterns available, data quality is of great importance in KDA applications. Recently, the authors have proposed to employ artificial rhythms and tempo cues to improve data quality: consistency and uniqueness of typing patterns. This paper examines whether improvement in uniqueness and consistency translates into improvement in authentication performance in real-world applications. In particular, we build various novelty detectors using typing patterns based on various strategies in which artificial rhythms and/or tempo cues are implemented. We show that artificial rhythms and tempo cues improve authentication accuracies and that they can be applicable in practical authentication systems. (C) 2009 Elsevier Ltd. All rights reserved.Kang P, 2008, COMPUT SECUR, V27, P3, DOI 10.1016/j.cose.2008.02.001Lee HJ, 2007, COMPUT SECUR, V26, P300, DOI 10.1016/j.cose.2006.11.006Lee HJ, 2006, PATTERN RECOGN LETT, V27, P1572, DOI 10.1016/j.patrec.2006.02.019Cho SZ, 2006, LECT NOTES COMPUT SC, V3832, P626Peacock A, 2004, IEEE SECUR PRIV, V2, P40, DOI 10.1109/MSP.2004.89He C, 2004, PATTERN RECOGN LETT, V25, P1389, DOI 10.1016/j.patrec.2004.05.004Yu EZ, 2004, COMPUT SECUR, V23, P428, DOI 10.1016/j.cose.2004.02.004Tax DMJ, 2004, MACH LEARN, V54, P45, DOI 10.1023/B:MACH.0000008084.60811.49Markou M, 2003, SIGNAL PROCESS, V83, P2481, DOI [10.1016/j.sigpro.2003.07.018, 10.1016/j.sigpro.2003.018]Markou M, 2003, SIGNAL PROCESS, V83, P2499, DOI 10.1016/j.sigpro.2003.07.019MARSLAND S, 2003, NEURAL COMPUTING SUR, V3, P157MONROSE F, 2002, INT J INFORMATION SE, V1, P69, DOI 10.1007/s102070100006Scholkopf B, 2001, NEURAL COMPUT, V13, P1443Japkowicz N, 2001, MACH LEARN, V42, P97HAYTON P, 2001, ADV NEUR IN, V13, P946Knorr EM, 2000, VLDB J, V8, P237Monrose F, 2000, FUTURE GENER COMP SY, V16, P351Cho S, 2000, J ORG COMP ELECT COM, V10, P295JAIN AK, 1999, BIOMETRICS PERSONALVAPNIK VN, 1998, STAT LEARNING THEORYdeRu WG, 1997, IEEE EXPERT, V12, P38, DOI 10.1109/64.642960Golfarelli M, 1997, IEEE T PATTERN ANAL, V19, P786, DOI 10.1109/34.598237POLEMI D, 1997, BIOMETRIC TECHNIQUESFrosini A, 1996, IEEE T NEURAL NETWOR, V7, P1482, DOI 10.1109/72.548175Gori M, 1996, PATTERN RECOGN LETT, V17, P241BISHOP C, 1995, NEURAL NETWORKS PATTBISHOP CM, 1994, IEE P-VIS IMAGE SIGN, V141, P217, DOI 10.1049/ip-vis:19941330BARNETT V, 1994, OUTLIERS STAT DATAUMPHRESS D, 1985, INT J MAN MACH STUD, V23, P263GAINES R, 1980, R256NSF RAND CORP

Free-text keystroke dynamics authentication with a reduced need for training and language independency

This research aims to overcome the drawback of the large amount of training data required for free-text keystroke dynamics authentication. A new key-pairing method, which is based on the keyboard’s key-layout, has been suggested to achieve that. The method extracts several timing features from specific key-pairs. The level of similarity between a user’s profile data and his or her test data is then used to decide whether the test data was provided by the genuine user. The key-pairing technique was developed to use the smallest amount of training data in the best way possible which reduces the requirement for typing long text in the training stage. In addition, non-conventional features were also defined and extracted from the input stream typed by the user in order to understand more of the users typing behaviours. This helps the system to assemble a better idea about the user’s identity from the smallest amount of training data. Non-conventional features compute the average of users performing certain actions when typing a whole piece of text. Results were obtained from the tests conducted on each of the key-pair timing features and the non-conventional features, separately. An FAR of 0.013, 0.0104 and an FRR of 0.384, 0.25 were produced by the timing features and non-conventional features, respectively. Moreover, the fusion of these two feature sets was utilized to enhance the error rates. The feature-level fusion thrived to reduce the error rates to an FAR of 0.00896 and an FRR of 0.215 whilst decision-level fusion succeeded in achieving zero FAR and FRR. In addition, keystroke dynamics research suffers from the fact that almost all text included in the studies is typed in English. Nevertheless, the key-pairing method has the advantage of being language-independent. This allows for it to be applied on text typed in other languages. In this research, the key-pairing method was applied to text in Arabic. The results produced from the test conducted on Arabic text were similar to those produced from English text. This proves the applicability of the key-pairing method on a language other than English even if that language has a completely different alphabet and characteristics. Moreover, experimenting with texts in English and Arabic produced results showing a direct relation between the users’ familiarity with the language and the performance of the authentication system