Continuous Authentications Using Frequent English Terms

Abstract

Most of the current computer systems authenticate a user’s identity only at the point of entry to the system (i.e., login). However, an effective authentication system includes continuous or frequent monitoring of the identity of a user already logged into a system to ensure the validity of the identity of the user throughout a session. Such a system is called a “continuous or active authentication system.” An authentication system equipped with such a security mechanism protects the system against certain attacks including session hijacking that can be performed later by a malicious user. The aim of this research is to advance the state-of-the-art of the user-active authentication research 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 including key duration, flight time latency, diagraph time latency, and word total time duration are analyzed. A series of experiments is performed to measure the performance of each feature individually as well as the results from the combinations of these features. More specifically, four machine learning techniques are adapted for the purpose of assessing keystroke authentication schemes. The selected classification methods are Support Vector Machine (SVM), Linear Discriminate Classifier (LDC), K-Nearest Neighbors (K-NN), and Naive Bayesian (NB). Moreover, this research proposes a novel approach based on sequential change-point methods for early detection of an imposter in computer authentication without the needs for any modeling of users in advance, that is, no need for a-priori information regarding changes. The proposed approach based on sequential change-point methods provides the ability to detect the impostor in early stages of attacks. The study is performed and evaluated based on data collected for 28 users. The experimental results indicate that the word total time feature offers the best performance result among all four keystroke features, followed by diagraph time latency. Furthermore, the results of the experiments also show that the combination of features enhances the performance accuracy. In addition, the nearest neighbor method performs the best among the four machine learning techniques.