On Accuracy of Classification-Based Keystroke Dynamics for Continuous User Authentication

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) diagraph time latency, and iv) word total time duration are analyzed. Two machine learning techniques are employed for assessing keystroke authentications. The selected classification methods are support vector machine (SVM), and k-nearest neighbor classifier (K-NN). 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.