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This study introduces a novel method for secure data transmission in cloud environments by integrating DNA cryptography with convolutional neural networks (CNNs). Leveraging deep learning, the system maps binary data to DNA sequences to generate robust cryptographic keys, ensuring highly secure encryption. The CNN model achieves exceptional accuracy (96.5% in training, 95.8% in validation) in binary-to-DNA conversion, enabling efficient key generation. These keys are then applied in an XOR-like encryption and decryption process, which demonstrates a large keyspace, strong key sensitivity, and ultra-fast decryption (0.002 s). The proposed approach not only enhances data security but also offers scalability and resilience, making it a practical and effective solution for cloud-based communication systems.

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Cloud-Based Secure Data Transfer Using Convolutional Neural Network (CNN) Enhanced DNA Cryptography

  • 章节

  • 发表日期：

    2026年

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  • M. Geetharani,
  • S. Muthuramalingam

摘要

This study introduces a novel method for secure data transmission in cloud environments by integrating DNA cryptography with convolutional neural networks (CNNs). Leveraging deep learning, the system maps binary data to DNA sequences to generate robust cryptographic keys, ensuring highly secure encryption. The CNN model achieves exceptional accuracy (96.5% in training, 95.8% in validation) in binary-to-DNA conversion, enabling efficient key generation. These keys are then applied in an XOR-like encryption and decryption process, which demonstrates a large keyspace, strong key sensitivity, and ultra-fast decryption (0.002 s). The proposed approach not only enhances data security but also offers scalability and resilience, making it a practical and effective solution for cloud-based communication systems.