In transmitting power-quality data, traditional encryption is insufficient and easily intercepted and tampered with. Directly transmitting real data can easily expose sensitive information, and plain text data without noise is prone to leakage, posing high privacy risks. To solve the security and privacy protection problems of power quality data transmission in the urban energy Internet, this article combined the Generative Adversarial Network (GAN) and Differential Privacy (DP) technologies to enhance data privacy protection. GAN can generate virtual data instead of real data, reducing the exposure of sensitive information, while utilizing DP to add Gaussian noise and Laplacian noise. The generated data can be compared with the original data, and various data attack methods have been simulated to test the data protection and privacy protection capabilities of the model. The model can be compared and analyzed in depth with other traditional data security transmission and protection models. The experiment shows that the GAN combined with DP technology model studied in this article achieved an accuracy of 95.6% between generated data and real data while preserving data privacy. The privacy budget of the model was 0.12, the noise intensity was 1.2, and the data risk probability was 0.04%. All of them are superior to other traditional models such as GAN, DP, federated generative adversarial network, and Local Differential Privacy Generative Adversarial Network (LDP-GAN). The model of GAN and DP combined in this article can effectively reduce the privacy risk of power quality data in urban energy Internet, and can resist multiple data attacks.
In the evolving landscape of telemedicine, ensuring the security of medical images has become increasingly critical. This paper introduces an advanced hybrid watermarking technique that integrates Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Singular Value Decomposition (SVD) using double encryption to safeguard medical images. The proposed methodology involves several steps: pre-processing medical images and watermarks, applying multiple levels of DWT decomposition, converting sub-bands to the frequency domain using DCT, and embedding the watermark into the singular values of DCT coefficients with SVD. The watermark is secured using circular and chaotic encryption techniques before embedding. The method's robustness, imperceptibility, and security were evaluated using a dataset of 100 medical images, each with a resolution of 512x512 pixels. Key performance metrics included Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Normalized Correlation (NC). Experimental results demonstrated high PSNR values (35.2 dB to 38.4 dB), indicating minimal distortion and excellent image quality. SSIM values between 0.92 and 0.96 highlighted the preservation of structural integrity, while MSE values ranged from 11.0 to 15.8, reflecting low distortion. NC values close to 1 indicated strong robustness, ensuring the watermark closely matched the original even after various image processing attacks. This hybrid method offers a robust solution for secure medical image watermarking, balancing effective embedding with image quality preservation. The integration of DWT, DCT, SVD, and double encryption enhances security and resilience, making it highly suitable for telemedicine applications.
Extreme climate in alpine regions leads to frequent failures of drop-out fuses, but existing methods have low detection accuracy and response speed. This article combines MKL-SVM (Multiple Kernel Learning-Support Vector Machine) and GRU (Gated Recurrent Unit) model to achieve precise fault diagnosis of drop-out fuses, leveraging their efficient processing of high-dimensional time series data. Sensors are used to collect data such as voltage, current, temperature, and wind speed. Based on GRU, global trends, temporal hidden states, and dynamic nonlinear features of the data are extracted as inputs for subsequent MKL-SVM. In MKL-SVM, a linear kernel function is used to simplify the linear relationship between static features; targeting temporal features, RBF (Radial Basis Function) kernel effectively captures dynamic trends, fluctuations, and periodic changes in temporal features. To further enhance the classification ability of the model, the kernel function parameters, including linear kernel weights and RBF kernel widths, are adjusted through cross-validation. Finally, based on the model diagnosis results and predicted data, an optimized operation and maintenance plan is formulated to reduce maintenance costs and improve equipment operating efficiency. The results show that the fusion model can significantly improve the reliability of fault detection and adapt to the unique environmental challenges of alpine regions. The accuracy of fault diagnosis for drop-out fuses is 95%, with an MSE (mean square error) value of about 0.15 and a maintenance cost reduction of about 21.89%. This method can achieve accurate fault diagnosis and efficient optimization of operation and maintenance under extreme weather conditions.
In response to the problems of complex data and insufficient correlation analysis in current network security situation assessment, this paper studies the combination of hypergraph convolutional network and improved traffic anomaly detection model. Firstly, Netflow, Snort, and Suricata detectors are utilized for network traffic monitoring and anomaly detection. By analyzing the correlation between selected network characteristics, the nodes and edges in the hypergraph are defined. Based on the complex interaction relationships between entities in the network, a hypergraph model is designed to determine the optimal node weights and connection patterns. Then the constructed hypergraph is input into the hypergraph convolutional network. Advanced feature representations of nodes are learned through the model, and network parameters are adjusted to achieve optimal extraction results. The information extracted by the hypergraph convolutional network is input into the support vector machine, and the accuracy of intrusion detection is improved through boundary partitioning. Finally, a hierarchical network security assessment system is adopted, covering three dimensions: service layer, host layer, and network layer. Simulated attack experiments are conducted for each layer to assess the network security situation of the three-layer architecture and the detection data of three detectors during the same time period. The experimental results indicate that the detection accuracy of the three detectors is 86.4%, 84.1%, and 81.3%, respectively.
Existing network security software generally has problems such as overly complex interface design, unreasonable navigation structure and imperfect error handling mechanism. This paper uses visual communication design methods to simplify the software interface, optimize the navigation structure and improve the error handling mechanism. In the interface simplification design, combined with the characteristics of network security software, a flat layout is adopted to reduce redundant security configuration items, optimize the layout of security functions, and highlight key security functions to reduce the user’s learning cost and risk of misoperation in complex security operations. When optimizing navigation, breadcrumb navigation and global search functions are added to facilitate users to quickly locate security settings and monitoring functions, and quick entrances to important security functions are added to the interface to improve the user’s response efficiency in emergency security incidents. By optimizing the display of security prompt information, the accuracy and guidance of error prompts are enhanced, and automatic recovery mechanisms and security log recording functions are applied, improving the system’s ability to handle security incidents and user response speed. After the interface is simplified, the time to update the software is reduced from 7 minutes to 4.5 minutes, and the navigation operation steps for professionals to handle system alarms are reduced by 50%. The approach adopted effectively reduces the interface complexity of network security software, optimizes the problem of improper navigation error handling, and significantly improves user experience and usage efficiency.
