Human-machine recognition based on mouse behavior modeling
Dong Xu, Xiaofeng Lu · 2023
User identity authentication of computer systems can be seen everywhere in daily life. Most systems use traditional onetime authentication methods, such as password, fingerprint and face recognition based on biometrics. However, there is a risk of password being cracked or forgotten, and fingerprint and face recognition require specific hardware devices. For the one-time authentication method, once the intruder passes the one-time authentication, the operation after the intrusion of the system will no longer be restricted. Human operation behavior can be regarded as a biological behavior feature, which cannot be stolen or copied, and can be used for identity authentication. If the user's mouse click and slide is abstracted into a behavior model, the model can be used to judge the user's identity and achieve user identity authentication. This paper studies human-machine recognition model based on mouse operation behavior. This paper deeply analyzes the advanced features of mouse click and slide, and uses machine learning algorithm to train mouse operation behavior model to identify whether the mouse is operated by human or software. This method can effectively detect the malicious behavior of hackers who use software to operate the mouse to automatically log in to the system.