Abstract
While the password-based authentication used in social networks, e-mail, e-commerce, and online banking is vulnerable to hackings, biometric-based continuous authentication systems have been used successfully to handle the rise in unauthorized accesses. In this study, an empirical evaluation of online continuous authentication (CA) and anomaly detection (AD) based on mouse clickstream data analysis is presented. This research started by gathering a set of online mouse-dynamics information from 20 participants by using software developed for collecting mouse information, extracting approximately 87 features from the raw dataset. In contrast to previous work, the efficiency of CA and AD was studied using different machine learning (ML) and deep learning (DL) algorithms, namely, decision tree classifier (DT), k-nearest neighbor classifier (KNN), random forest classifier (RF), and convolutional neural network classifier (CNN). User identification was determined by using three scenarios: Scenario A, a single mouse movement action; Scenario B, a single point-and-click action; and Scenario C, a set of mouse movement and point-and-click actions. The results show that each classifier is capable of distinguishing between an authentic user and a fraudulent user with a comparatively high degree of accuracy.
Highlights
In the current age of internet technology, authentication is a major security issue because authentication failures often cause detrimental effects
The team conducted a set of experiments in order to validate the effectiveness of machine learning and deep learning techniques (decision tree (DT), k-nearest neighbor (KNN), random forest (RF), and convolutional neural network (CNN) classifiers) using the 87 features extracted from the raw mouse data
Mouse dynamics are behavioral biometrics that can be applied in different security fields such as human identification
Summary
In the current age of internet technology, authentication is a major security issue because authentication failures often cause detrimental effects. Behavioral biometrics analysis of a person’s keyboard usage behavior does not require access to the person’s sensitive data These advantages have led to an increase on research on the use of mouse dynamics biometrics in user-authentication systems [3,8,9]. Mouse dynamics is one of the behavioral biometrics that can save and analyze actions from a mouse input device such as general movement, drag-and-drop, and point-click actions while a person interacts with a specific graphical user interface [5,10] Both online CA and AD techniques are other promising techniques that are capable of addressing the vulnerability in a static one-time authentication.
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