由於多元化的資訊發展與環境，盜用他人的帳號密碼執行不合法的犯罪行為頻傳，顯示過去採取一次性登入(single sign-on)做為電腦系統安全認證的方式已經不敷使用。因此，許多學者分別提出以滑鼠操作軌跡(Trajectory)為基礎的辨識/識別技術做為輔助。目前主要區分成Histogram與Mouse Dynamics兩類技術。然而，Histogram的辨識/識別能力會受到應用程式與工作環境的影響。Mouse Dynamics的部分，則因為過去的軌跡樣本收集方式，一部份會中斷使用者的正常工作；另一部分則是大量收集各種軌跡資料，其內容複雜且經常包含無辨識能力或影響辨識/識別的雜訊問題。因此，本團隊提出收集—指定應用程式且指定功能—的滑鼠操作軌跡的方式以純化樣本資料並提高識別的準確率。In recent years, with the popularization of computer and network, the single sign-on approach of the tradition authentication mechanism may not enough to protect our cyber assets in the future since the illegal cybercrimes access is still on the increase. Consequently, many researchers propose a re-authentication concept that uses the mouse dynamics to verify the user's identity. At the present time, mouse trajectories were collected from all applications or a specified application (such as Internet Explorer) for implementing verification models. However, the mouse trajectories include many types of activity and noise that will affect the verification accuracy. Therefore, this thesis proposes a method that only obtains the specific mouse trajectories to category trajectories and to filter noise for improving the verification accuracy of the implemented models. To prove the proposed method, this thesis conducts experiments to compare the verification rate from three types of trajectories collection. The experiment results show that the accuracy of the proposed method is better than the others. The successful verification rate in our model is 94%. This thesis technique can be applied to existing environmental and upgrade computer security.
