A computer network communication data encryption method based on improved MD5 algorithm

The traditional communication data encryption methods rely on complete time series to extract encryption features, making them susceptible to data noise and resulting in high packet loss rates. To address these issues, this study introduces an enhanced MD5 algorithm for encrypting network communication data. Initially, a Huffman single item reassembly encryption structure is established to create a deployment of multi-objective fine-grained encryption nodes. Subsequently, a sliding homomorphic mixed encryption model is developed, along with derived network communication encryption instructions. This is followed by the use of overlapping deep learning algorithms to achieve mixed encryption of network communication data. Compared to encryption methods based on ring topology and VPN technology, the proposed encryption structure is more flexible, variable, and exhibits dynamic accounting verification characteristics. The findings indicate that there are fewer encryption discrepancies in practical applications, and the data maintains higher stability during the mixed encryption process.