Password Complexity Prediction Based on RoBERTa Algorithm

Abstract

Corresponding author email: In the digital age, password security is a top priority for protecting personal information. Machine learning techniques provide us with intelligent and efficient means to enhance password security. In this paper, we adopt RoBERTa algorithm and use the password complexity text dataset for password complexity prediction, and the confusion matrix and accuracy rate of the three classifications are derived through two model trainings. The confusion matrix shows that the vast majority of the classification results are accurate, and the accuracy of the two classifications is over 99.741% and 99.11%, respectively. This indicates that the model is able to effectively predict password complexity, provide users with accurate feedback, and prompt users to enhance password security in a timely manner. Through this study, we can better understand how to use machine learning technology to improve password security and protect personal private information from malicious intrusion. In our daily life, we should pay attention to the complexity of password settings and realise the importance of password security for personal information protection. We look forward to the launch of more similar studies in the future to further strengthen cybersecurity protection measures and work together to build a more secure and reliable digital environment.