Abstract
Data security in information collection systems faces challenges such as malicious attacks, data leaks, and delayed disaster recovery. This paper proposes a data security model for disaster recovery in information collection systems based on Artificial Intelligence (AI) encryption algorithms. By introducing a dynamic encryption algorithm driven by Deep Learning (DL), this model achieves real-time secure encryption and intelligent key management for collected data. First, feature extraction is performed on the data stream, and a Convolutional Neural Network (CNN) is used to identify abnormal access behavior, triggering a multi-factor dynamic encryption mechanism. Second, a Generative Adversarial Network (GAN) is used to check the integrity of backup data to prevent tampering and loss. Finally, distributed key storage and access auditing are implemented based on blockchain technology. The proposed model maintains a data encryption speed of 3.1–3.3 ms, a recovery efficiency of 4.09 ms, and a data integrity verification accuracy of 99.5%. This approach effectively improves the security and recovery reliability of disaster recovery data in information collection systems, providing a new approach for data security assurance.
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