Password strength evaluation model based on ensemble learning

Abstract

The invention designs a password strength evaluation model based on multi-model ensemble learning. Firstly, a real password training set is used to train several existing password evaluation models assub-models. Secondly, several trained sub-models are used as base learners for ensemble learning, and the advantage integration of each sub-model is realized by the combination strategy of partial weak-term voting. Finally, a universal password evaluation model is implemented based on high accuracy. Password strength evaluation based on a multi-model ensemble learning model for passwords with different levels of complexity is performed, the evaluation results are high in accuracy and versatility, the multi-model ensemble learning has good applicability in password evaluation.

Description

technical field [0001] The invention belongs to the field of information security. Aiming at the problem that the existing password evaluation models are poor in versatility, and there is no evaluation model that can be used from simple passwords to very complex passwords, a password evaluation model based on multi-model ensemble learning is designed. Background technique [0002] In terms of application system authentication, password security is directly related to the security of the entire application system and the protection of user privacy. With the development of Internet services (such as mail, e-commerce, social networking, etc.), more and more network services require password protection. However, human beings have limited memory capacity, which leads users to inevitably use different degrees of weak passwords, or use the same password in different application systems, which brings serious security risks to application systems (such as social engineering attacks,...

AI Technical Summary

Problems solved by technology

[0005] The existing password evaluation models have poor versatility, and there is...

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