Internet of things identity continuous verification method based on multi-mode technology

By continuously collecting and analyzing user's facial images and keyboard input mode data on IoT devices, and using deep learning for cross-modal correlation learning, the problem of unsustainable identity verification in the prior art is solved, and security and user experience are improved.

CN120046133AInactive Publication Date: 2025-05-27STATE GRID HENAN INFORMATION & TELECOMM CO
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Patent Information

Application Number
CN202510056777.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-01-14
Publication Date
2025-05-27
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The existing IoT authentication method based on multimodal technology does not conduct continuous verification after the user logs in for the first time, which poses a security risk. At the same time, the traditional continuous verification method will reduce the user experience.

Method used

By continuously collecting facial image data and keyboard input mode data when users interact with IoT devices, deep learning technology is used to extract facial semantic features and keyboard usage behavior mode features, and perform cross-modal association learning interaction fusion to achieve continuous verification of user identity.

Benefits of technology

Continuous monitoring and verification of user identities is realized, the security of IoT devices is improved, and the user experience decline caused by traditional continuous verification methods is avoided.

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Abstract

The invention relates to the technical field of identity continuous verification, and particularly discloses an Internet of Things identity continuous verification method based on a multi-mode technology, which comprises the following steps: continuously collecting face image data and keyboard input mode data of a user in an interaction process of the user and Internet of Things equipment; the deep learning technology is adopted to process the face image data and the keyboard input mode data of the user to extract the face semantic features and the keyboard use behavior mode features of the user, and then cross-modal association learning interaction fusion is carried out on the face features and the keyboard use behavior mode features of the user, so that the user experience is improved. Therefore, the potential association between the two is captured, and the continuous verification of the user identity is realized. Through the mode, the behavior of the user can be continuously monitored, and an alarm is given out in time when the identity of the user is abnormal, so that the safety of the Internet of Things equipment is effectively improved, and meanwhile, the problem that the user experience is reduced possibly caused by a traditional continuous verification method is avoided.
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