TY - GEN
T1 - On accuracy of keystroke authentications based on commonly used English words
AU - Darabseh, Alaa
AU - Namin, Akbar Siami
N1 - Publisher Copyright:
© 2015 Gesellschaft für Informatik e.V.
PY - 2015/10/30
Y1 - 2015/10/30
N2 - The aim of this research is to advance the user active authentication using keystroke dynamics. Through this research, we assess the performance and influence of various keystroke features on keystroke dynamics authentication systems. In particular, we investigate the performance of keystroke features on a subset of most frequently used English words. The performance of four features such as i) key duration, ii) flight time latency, iii) digraph time latency, and iv) word total time duration are analyzed. Experiments are performed to measure the performance of each feature individually as well as the results from the different subsets of these features. Four machine learning techniques are employed for assessing keystroke authentications. The selected classification methods are two-class support vector machine (TC) SVM, one-class support vector machine (OC) SVM, k-nearest neighbor classifier (K-NN), and Naive Bayes classifier (NB). The logged experimental data are captured for 28 users. The experimental results show that key duration time offers the best performance result among all four keystroke features, followed by word total time. Furthermore, our results show that TC SVM and KNN perform the best among the four classifiers.
AB - The aim of this research is to advance the user active authentication using keystroke dynamics. Through this research, we assess the performance and influence of various keystroke features on keystroke dynamics authentication systems. In particular, we investigate the performance of keystroke features on a subset of most frequently used English words. The performance of four features such as i) key duration, ii) flight time latency, iii) digraph time latency, and iv) word total time duration are analyzed. Experiments are performed to measure the performance of each feature individually as well as the results from the different subsets of these features. Four machine learning techniques are employed for assessing keystroke authentications. The selected classification methods are two-class support vector machine (TC) SVM, one-class support vector machine (OC) SVM, k-nearest neighbor classifier (K-NN), and Naive Bayes classifier (NB). The logged experimental data are captured for 28 users. The experimental results show that key duration time offers the best performance result among all four keystroke features, followed by word total time. Furthermore, our results show that TC SVM and KNN perform the best among the four classifiers.
UR - http://www.scopus.com/inward/record.url?scp=84959512538&partnerID=8YFLogxK
U2 - 10.1109/BIOSIG.2015.7314612
DO - 10.1109/BIOSIG.2015.7314612
M3 - Conference contribution
AN - SCOPUS:84959512538
T3 - Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)
BT - BIOSIG 2015 - Proceedings of the 14th International Conference of the Biometrics Special Interest Group
A2 - Busch, Christoph
A2 - Uhl, Andreas
A2 - Bromme, Arslan
A2 - Rathgeb, Christian
A2 - Busch, Christoph
A2 - Uhl, Andreas
A2 - Bromme, Arslan
A2 - Rathgeb, Christian
PB - Gesellschaft fur Informatik (GI)
T2 - 14th International Conference of the Biometrics Special Interest Group, BIOSIG 2015
Y2 - 9 September 2015 through 11 September 2015
ER -