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首页> 外文期刊>International Journal of Industrial Ergonomics >Identification of trusted interactive behavior based on mouse behavior considering web User's emotions
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Identification of trusted interactive behavior based on mouse behavior considering web User's emotions

机译：考虑Web用户的情绪的鼠标行为识别可信互动行为

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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.
机译：在现有的网络安全技术下，黑客仍然可以冒充合法用户并侵犯恶意破坏系统。因此，该研究构造了用户独特的鼠标行为模式，以确定真实环境中的可信交互行为，并量化不同情绪对鼠标行为的影响以及用户可信交互行为识别的准确性。首先，在学术研究网站（AML）上收集鼠标数据8个用户可信赖的交互。这些数据用于通过称为随机林的大数据分析方法构建基本可信交互模型。其次，在一项重复的测量实验中，18名参与者在不同情绪下完成了AML上的任务，并分析了对用户可信任识别的鼠标行为和准确性的影响。在结果中，基于鼠标行为的可信相互作用行为识别的准确性达到91.82％，错误率低于8.18％。在不同情绪下，在水平速度，速度和行驶距离中观察到显着差异。但是，在不同情绪下，用户可信任行为识别的准确性没有显着差异。基于这些结果，可以基于用户的鼠标行为模式准确地识别Web用户的可信交互行为。用户的鼠标行为在不同的情绪下不同，但对用户可信任行为的识别没有显着差异。调查结果有助于为网络信息安全提供另一种保护层。

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