Database security intrusion behavior identification method based on mapping deep learning
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Abstract
In order to solve the problem of database security intrusion identification and rationally plan the query path of database index, a method of database security intrusion identification based on mapping deep learning is proposed. A database security intrusion identification framework is constructed. Authorized users are grouped and calibrated according to the security level. The weights of plaintext keywords are obtained by mapping deep learning algorithm, and the keys of ciphertext are obtained. The encrypted results are uploaded to the main server, and the index of ciphertext is obtained by mapping deep learning algorithm, so as to realize database security intrusion identification. The experimental results show that the proposed method is more real-time for encrypting and decrypting a large number of data in the database, and the maximum recognition rate is 95%, which shows that the proposed method has good recognition effect.
Publication details
- DOI
- 10.1117/12.3011574
- OpenAlex
- W4388488299
- Document type
- conference-paper
- Language
- EN
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