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.
Ransomware attacks serve as an example of how cyber adversaries provide a constant and changing threat in the digital age, making it imperative to fully comprehend their strategies and take preventative action to reduce risks. This literature review aims to provide a comprehensive examination of BlackCat, confining its focus to the malware's origins, methodologies, selected targets, identified victims, the far-reaching consequences it inflicts upon affected entities, and techniques and strategies to mitigate BlackCat risks. By looking at this, the study hopes to provide cybersecurity experts and companies with the information they need to strengthen their defenses against the growing threat of ransomware. This paper revealed a perceived vulnerability of educational institutions to ransomware attacks, with a moderate level of confidence in the cybersecurity measures implemented by these institutions. Survey respondents emphasized the importance of measures like Two-Factor Authentication (2FA) and regular cybersecurity training as key strategies for better preparation against ransomware threats.
The existing analysis of cultural communication metaphor patterns in online social networks is limited to specific platforms, resulting in a one-sided understanding of cultural communication metaphors, and the data processing and storage process when analyzing metaphor patterns is prone to user privacy leakage. This paper introduced a cluster analysis and data privacy protection strategy to collect user-generated content from multiple online social platforms such as Weibo, WeChat, Facebook, and Douyin to ensure sample diversity. The Laplacian mechanism is used before data analysis to ensure data privacy by adding noise. Natural language processing technology is applied to use the BERT (Bidirectional Encoder Representations from Transformers) model to perform deep semantic analysis on text and identify metaphorical expressions in cultural communication. The K-means clustering algorithm is used to classify the extracted metaphor feature vectors, and different K values are set for experiments. The optimal number of clusters is determined by the elbow rule. The experimental data shows that after cluster analysis, the optimal number of clusters of the extracted feature vectors is 5, which are ”social identity,” ”emotional resonance,” ”transmission of cultural symbols,” ”expression of values” and ”cross-cultural communication,” When the optimal privacy budget is set to 0.5 during cluster analysis, the risk value reaches the minimum 0.008, and the analysis accuracy is 0.98. The data proves the effectiveness of the cluster analysis and data privacy protection strategy studied in this paper.
The network security model based on static rules lacks flexibility and adaptability, and it is difficult to adapt to and respond to dynamically changing network threats in real-time. Therefore, this paper proposes a method for optimizing the decision-making process of digital twins (DTs) in network security based on graph neural networks (GNNs). First, this paper applies the Adam optimizer to adjust the learning rate by optimizing the GNN structure, combines the cross entropy loss function to improve the attack recognition ability, and uses L2 regularization and Dropout to prevent overfitting to enhance the model’s performance in complex network data. Then, real-time network threat detection and attack path prediction are performed based on the optimized GNN model. To further improve the intelligent level of network security protection, this paper applies the online learning (OL) algorithm to continuously update the model to adapt to changes in the network environment and threat patterns. At the same time, combined with the policy gradient (PG) method, an intelligent decision-making module is designed to automatically adjust the defense strategy to achieve dynamic protection against changing network threats. Experimental results show that the optimized GNN model’s accuracy in network threat detection reaches 93.3%, which is 9.7% higher than that of the non-DT model, and the malware’s precision is increased by 6.9%. The system’s response time is reduced to 50ms, which significantly improves the real-time performance and decision accuracy of network security protection and demonstrates the excellent performance and broad application prospects of this method in dynamic network environments.
Aiming at the data transmission security of medical service hybrid robots and the privacy leakage of cloud data storage, this paper applies a comprehensive solution combining convolutional neural networks and kinematic models. Global and local coordinate systems are established to uniformly represent the relative position and posture of the robot end effector and each joint, and convolutional neural networks are used to improve the robot’s environmental perception and navigation efficiency. While improving the robot’s adaptability, the privacy budget of differential privacy is used to add data noise effects, and the AES (Advanced Encryption Standard) encryption algorithm is used to protect sensitive data. During data transmission, SSL/TLS (Secure Sockets Layer/Transport Layer Security) protocols are used to ensure data security. In order to improve system performance and scalability, microservice architecture and containerization technology are used to achieve service decoupling and independent deployment, and optimize the response time and processing efficiency of cloud computing architecture. Experimental results show that the average network security vulnerability detection rate of the system for 7 security areas is 0.71, and the probability of data leakage is only 1% when the privacy budget is 0.1. This method successfully guarantees data security and user privacy.
In response to the privacy leakage risk and low-efficiency issues of traditional data access control, especially in distributed environments, data transmission and centralized processing may expose sensitive information. To improve data access control in information platforms, ensure data security, and accelerate global model training, this article introduced a joint mechanism of differential privacy and FedAvgM (Federated Averaging with Momentum) optimization to enhance privacy protection and improve training efficiency. Firstly, the Laplacian noise mechanism was adopted to prevent user privacy information from being leaked during global model training by adding noise to the dataset, ensuring the privacy of each data access. Then, combined with the FedAvgM optimization algorithm, distributed nodes calculated local model parameters separately and merged these parameters through weighted averaging to reduce the training time of the global model and improve efficiency. Finally, layered encryption technology was adopted to add multiple layers of encryption during data transmission to ensure the security of the transmission link. At the same time, a dynamic permission allocation mechanism was introduced to limit the frequency of access to sensitive data. The experimental results demonstrate that when the privacy budget at ε=20, the privacy protection method of Laplace mechanism still maintains 73% accuracy in data transmission, with a privacy leakage risk of only 0.27. Under the same training epochs, the FedAvgM optimization algorithm achieved a correct data transmission accuracy of 97%. In comparison, the convergence speed increased by 0.29%/min in 1-20 training epochs, showing a faster convergence speed. Under four different noise attack methods, the anti-noise ability of layered encryption is the lowest at 15.4 dB, 18.5 dB, 17.1 dB, and 17.3 dB, respectively. This method effectively improves the problems of privacy leakage risk and low efficiency in traditional data access control.
This paper aimed to address the shortcomings of current college English learning platforms in user behavior analysis and security protection, especially the lack of accurate abnormal behavior detection and dynamic identity authentication measures. By adopting DGCN (Dynamic Graph Convolutional Network) and XGBoost (eXtreme Gradient Boosting) classification models for abnormal behavior detection, a dynamic identity authentication optimization strategy based on behavior risk scoring is proposed to improve the security of the platform. The user embedding features output from DGCN are input into XGBoost for user behavior classification. Based on the predicted probability of the XGBoost classification model, a risk score is generated for each user, and different identity authentication strategies are defined according to the risk score range. Experimental results show that by modeling multi-layer dynamic graph structures, the accuracy, precision, recall and F1 score of the XGBoost model reached 97.6%, 97.5%, 95.3%, and 96.4% respectively. In addition, when the step size of the DGCN model increases from 1 to 10, its accuracy only slightly decreases from 97.6% to 97.5%, and the user behavior classification performance is stable. The incidence of account theft with dynamic identity authentication is maintained between 3% and 8%, and the average authentication time is increased by 2.1 seconds compared with the traditional password authentication method. The proposed DGCN can optimize the security strategy of the college English learning platform based on the user behavior risk score, and it can select targeted identity authentication strategies according to the user's risk score.
User and Entity Behavior Analytics has been one of the key steppingstones towards protecting valuable data and information for organizations that decided to store their assets in a cloud-based storage. The implications suggest that third-party software may not be as reliable as these are prone to attacks due to its accessibility and vulnerable nature. This study aims to provide a comprehensive review that covers User and Entity Behavior Analytics (UEBA) and other machine learning techniques to effectively mitigate cyberattacks and protect organizational assets from cloud-based security threats. The objective is to identify and analyze anomalous user and entity behaviors that may indicate potential cyberattacks and provide recommendations for organizations to enhance their cloud security posture and minimize the risk of data breaches and other security incidents. The research covers some of the algorithms used in detecting anomalies such as Isolation Forest, Deep Autoencoder, and Linear Regression, and emphasizes the adaptability of these chosen algorithms to the dynamic landscape of cyber threats, especially in cloud environments. By arriving at a cohesive integration of machine learning algorithms, this study advocates for a holistic approach that aligns with evolving security challenges. Ultimately, the significance lies in offering a nuanced perspective on effective cyber threat mitigation strategies, contributing to the broader conversation on securing organizational assets in the face of evolving cybersecurity landscapes.
Existing network information flow advertising mostly relies on user behavior data, which is easy to cause user privacy leakage and low precision of advertising delivery. This article combines user behavior data encryption and deep learning technology to propose a method for optimizing the effect of network information flow advertising. The user behavior data is encrypted using Paillier homomorphic encryption technology. Then, the stacked auto encoder (SAE) is used to extract high-order nonlinear features, and the shallow features are automatically combined based on Field-aware Factorization Machines (FFM) to construct a SAE-FFM comprehensive model. At the same time, dropout is used to reduce model overfitting, and the advertising effect is optimized through two-stage pre-training and fine-tuning. The experimental results show that when the number of impressions is 10,000, the average advertising click through rate and conversion rate of the SAE-FFM model are 4.50% and 0.59% respectively, and the information leakage rate is 0.052%. The results show that the SAE-FFM model can greatly improve the click through rate and conversion rate of advertisements, prevent privacy leakage, and help the development of the information flow advertising industry.
Since of the development of advanced devices, cybersecurity is presently an vital portion of both trade and government. "Making Comprehensive Cybersecurity Approaches: Adjusting Organize Security and Compliance with Security Laws" talks around how difficult it is to create cybersecurity frameworks that secure systems well whereas moreover taking after strict security laws. As the dangers against businesses change rapidly, they have to be put in put rules that lower risks like data hacks, ransomware, and dangers from interior the company. But they also have to be taken after protection rules like GDPR and HIPAA, which push to protect information, get consent from clients, and be open and legitimate. This exposition looks at how cybersecurity strategies and taking after the law can work together, centring on the leading ways to adjust desires of organizations for security with people's rights. It talks around critical things like encryption; get to rules, emergency activity plans, and ways to keep information as little as conceivable. By looking at real-life case ponders and administrative frameworks, this paper points to allow policymakers valuable data for making rules that are both secure and legitimate.
Securing crucial infrastructure, which incorporates ranges like vitality, transportation, healthcare, and finance, is exceptionally imperative since it plays an enormous portion in keeping the nation secure and secure. This paper study about how cybersecurity approaches and law necessities work together to ensure safeguard the critical resources from modern cyber threats. It gives an intensive approach toward the rules and directions that control hacking in critical areas, centring on the ones that must be taken after and universal guidelines of cybersecurity policies. The paper also discuss about the need for network security and centres on ways to form systems more versatile, such as through hazard evaluations, risk data, and occurrence reaction methods. Modern innovations like AI and blockchain are looked at to see how they could be able to form critical frameworks more secure. At the same time, the issues of lawful compliance, national issues, and information protection are carefully considered. This study considers supportive since it combines legitimate necessities and specialized security measures. It appears how to form solid cybersecurity plans that take after national and universal rules. These plans will ensure basic framework and keep operations running easily whereas moreover being legitimately mindful.
NIS2 will soon come into force all over the EU and – much like the GDPR – it threatens large fines for those that don’t comply. However, it may serve as a much-needed wake up call for those who are overlooking the fastest-growing part of their attack surface – the mobile device.
Networks are becoming ever-more complex, and people with the right skills to manage them securely are in desperately short supply. However, with the right network management environment – one that supports automation and operational efficiency – supported by relevant training, you can make your networks more resilient to current and future threats.
Cyber attacks are on the increase, in terms of both the number of attacks and the damage they cause. Encouraging organisations to improve their cyber security has had only a limited effect. Now it’s the turn of legislation and regulation, with the Cyber Resilience Act (CRA) and Network and Information Security Directive (NIS2) coming into force in the EU. But what kind of impact are they likely to have?
UK organisations – and perhaps most notably the National Health Service (NHS) – are the focus of serious overhauls in IT provision and strategy. But it seems that far too little of the effort and expense is geared towards security. While increased efficiency and productivity are important, any gains in those areas are doomed to be undermined if security is not an intrinsic part of IT improvement programmes.
A cyber attack can have particularly devastating results on manufacturing organisations. More than half have turned to external providers to fully manage their security, while many more outsource at least some operations. But are the security service providers up to the task? And how can a manufacturer better align security with the organisation and its specific requirements?
