Traditional polygraph techniques mostly rely on the changes of an individual\'s physiological indicators, such as electrodermal activity, heart rate, breath, eye movement and function of neural signals and other indicators. They are easily affected by individual physical conditions, counter-tests, external environment and other aspects, and it is difficult to conduct large-scale screening tests based on the traditional polygraph techniques. The application of keystroke dynamics to polygraph can overcome the shortcomings of the traditional polygraph techniques to a large extend, increase the reliability of polygraph results and promote the validity of legal evidence of polygraph results in forensic practice. This paper introduces keystroke dynamics and its application in deception research. Compared with the traditional polygraph techniques, keystroke dynamics can be used with a relatively wider application range, not only for deception research but also for identity identification, network screening and other large-scale tests. At the same time, the development direction of keystroke dynamics in the field of polygraph is prospected.
传统的测谎技术大多依赖个体的生理指标，如皮肤电、心率、呼吸、眼动和神经信号功能等指标的变化，容易受个体身体条件、反测试以及外部环境等方面的影响，并且很难进行大面积的筛查测试。将击键动力学应用于测谎可以很大程度上克服传统测谎技术的不足，增加测谎结果的可靠性，促进测谎结论在司法鉴定实践中的法定证据效力。本文介绍了击键动力学及其在欺骗研究中的应用，和传统测谎技术相比，击键动力学除了可以进行欺骗行为研究外，还可用于身份识别、网络筛查等大面积的测试，应用范围相对更广。同时，本文还对击键动力学在测谎领域的发展方向进行了展望。.

In order to improve user authentication accuracy based on keystroke dynamics and mouse dynamics in hybrid scenes and to consider the user operation changes in different scenes that aggravate user status changes and make it difficult to simulate user behaviors, we present a user authentication method entitled SIURUA. SIURUA uses scene-irrelated features and user-related features for user identification. First, features are extracted based on keystroke data and mouse movement data. Next, scene-irrelated features that have a low correlation with scenes are obtained. Finally, scene-irrelated features are fused with user-related features to ensure the integrity of the features. Experimental results show that the proposed method has the advantage of improving user authentication accuracy in hybrid scenes, with an accuracy of 84% obtained in the experiment.