Database security intrusion behavior identification method based on mapping deep learning

Zixian Dong; Jin Pang; Wei Hu; Yating Gao; Jinmeng Zhao; Xinyu Liu

doi:10.1117/12.3011574

8 November 2023 Database security intrusion behavior identification method based on mapping deep learning

Zixian Dong, Jin Pang, Wei Hu, Yating Gao, Jinmeng Zhao, Xinyu Liu

Proceedings Volume 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023); 129232K (2023) https://doi.org/10.1117/12.3011574
Event: 3rd International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV 2023), 2023, Chongqing, China

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.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zixian Dong, Jin Pang, Wei Hu, Yating Gao, Jinmeng Zhao, and Xinyu Liu "Database security intrusion behavior identification method based on mapping deep learning", Proc. SPIE 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023), 129232K (8 November 2023); https://doi.org/10.1117/12.3011574

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