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A method of transmission of highly confidential information in power communication network based on digital watermarking technology is proposed in order to reduce the leakage risk of power information and achieve the purpose of safe transmission of private information. This method establishes the data topology model of power communication network and connects the data receiving and sending terminals. Digital watermarking technology is used to embed digital watermarking in the information to be transmitted, encrypt the private information, and optimize the DES encryption algorithm to encrypt the information twice, so as to realize the safe transmission of information. The experimental results show that the digital watermark embedding rate, DES encryption rate, DES decryption rate and watermark extraction rate of the proposed method are all above 90 Mbps, and the file transfer time of 3965Byte is less than 5s, leakage risk rate and packet loss rate are 0.0001% and 0.006%, respectively, which effectively protects the security of high privacy information in the power communication network and prevents information leakage. [ABSTRACT FROM AUTHOR]

With the increasing number of financial transactions, financial fraud has become increasingly serious for financial institutions and the public. The core idea of this model is to integrate multiple neural network structures and utilize their respective advantages to improve the performance of fraud detection. Firstly, we employed the convolutional neural network with interpretable blocks (CNNIB) convolutional neural network (CNN) to extract key features from the data to capture patterns and patterns in fraud cases. Secondly, we introduced the autoencoder generative adversarial network (AE-GAN) adversarial network to perform feature analysis on sequence data to capture temporal features in transaction sequences. Finally, we used differential detection for classification to determine whether transactions were fraudulent. An independent detection module was established to accelerate the recognition of financial fraud, and parameter indicators were optimized. Finally, a hybrid neural network model was established. The experimental results indicate that our model has achieved significant results in quickly detecting financial fraud; compared with traditional single neural network models, hybrid neural network models have significant improvements in accuracy and efficiency. In addition, we conducted in-depth analysis of the model and revealed its performance stability under different training set sizes and data distributions. Our research findings provide an effective tool for financial institutions to quickly identify financial fraud. [ABSTRACT FROM AUTHOR]