User Classification Based On Mouse Dynamic Authentication Using K-Nearest Neighbor

Mouse dynamics authentication is a method for identifying person by analyzing the unique pattern or rhythm of their mouse movement. Owing to its distinctive properties, such movements can be used as basis security. The development technology followed urge keep private data safe from hackers. Therefore, increasing accuracy user classification and reducing false acceptance rate (FAR) are necessary improve In this study, we propose combine K-nearest neighbor simple random sampling obtain sample dataset users attackers. results show that our proposed has high implement practical system reports best than previous research with FAR 0.037. implemented in real login system. rejection will not problem because most important thing denying attacker access.