Continuous Authentication of Mouse Dynamics Based on Decision Level Fusion

The demand for information security is growing with the changes of the times, and the authentication system is an important gateway to ensure information security. Password authentication is the most commonly used authentication method in modern network. However, because the password is easy to be cracked, we need to pay more attention to more authentication methods. In many authentications, the advantage of keystrokes and mouse authentication are more obvious; however, when researchers use mouse dynamics to authenticate, classifier training always require a large amount of data, and when the data is less, there may be inaccurate results. In this paper, a decision-level fusion method of the two classifiers is proposed, which reduces the strong dependence on data during training. In this method, the support vector machine optimized by genetic algorithm and k-nearest-neighbor algorithm are combined to get a lower error rate, which is lower than the error rate generated by the two methods alone.