Identification of trusted interactive behavior based on mouse behavior considering web User's emotions

Under existing network security technology, it is still possible for hackers to impersonate legitimate users and invade a system for malicious destruction. Therefore, this study constructs a user's unique mouse behavior pattern to identify a trusted interaction behavior in a real environment and quantify the effects of different emotions on mouse behavior and the accuracy of the user's trusted interaction behavior identification. First, mouse data was collected for 8 user's trusted interactions on an academic study website (AML). These data were used to construct the basic trusted interaction model by a big data analysis method called a random forest. Second, in a repeated measurement experiment, 18 participants completed tasks on the AML under different emotions, and the emotions' impact on the mouse behavior and accuracy of the user's trusted interaction identification was analyzed. In the results, the accuracy of the trusted interaction behavior identification based on mouse behavior reached 91.82%, and the error rate was lower than 8.18%. Significant differences were observed in horizontal velocity, velocity, and traveled distance under different emotions. However, there was no significant difference in the accuracy of a user's trusted interaction behavior identification under different emotions. Based on these results, the trusted interaction behavior of web users can be accurately identified based on the user's mouse behavior pattern. The user's mouse behavior differs under different emotions, but there is no significant difference on the identification of the user's trusted interaction behavior. The findings help to provide another protection layer for network information security.