Network Security Protection Research Articles

Digital Preservation and Network Security Protection: A Case Study of Landscape Painting in Ming Dynasty

In the global wave of digitalization, the digital preservation of Chinese Ming Dynasty landscape paintings has achieved remarkable results. According to statistics, major museums in China have scanned more than 60% of Ming Dynasty landscape paintings with high precision, and the digital preservation work covers a wide range. However, the accompanying cybersecurity challenges cannot be ignored either. In recent years, cyber attacks on digital platforms of cultural heritage have occurred frequently. Worldwide, the number of such attacks has exceeded 3 million every year, of which attacks on works of art account for as high as 15%. Faced with this grim situation, China has taken a series of measures to strengthen network security protection. By upgrading encryption technology, optimizing network security protection systems, and conducting regular network security training and drills, China has made remarkable progress in ensuring the security of digital works of art. According to the latest data, since the implementation of enhanced protection measures, the number of cyber attacks on the digital platform of landscape painting in Ming Dynasty in China has decreased by 35%, effectively protecting precious cultural heritage from cyber threats.

Research on University Network Data Anomaly Detection and Security Protection Algorithm Based on edge computing

Research on University Network Data Anomaly Detection and Security Protection Algorithm Based on edge computing

The role of cybersecurity in women’s involvement in online sexual violence and its response strategies

With the rapid development of information technology, cyberspace has become an indispensable part of people’s daily lives. However, cyberspace is not a pure land, especially with the increasingly prominent issue of online sexual violence against women. This article explores the role of cybersecurity in women’s involvement in online sexual violence and its response strategies. The article first analyzes how cyberspace has become an important platform for people’s lives and work in the background of the popularization of the Internet and social media. Still, it also brings about network security problems, especially the increasingly serious phenomenon of online sexual violence against women. These behaviors seriously violate women’s privacy and reputation rights and cause significant harm to their mental health. The article demonstrates the widespread and severe nature of online sexual violence through specific cases and statistical data. At the same time, the article also analyzed the causes of online sexual violence, including insufficient regulation of social media platforms, information overload of personalized algorithm recommendations, and the impact of user emotional intelligence on online behavior. Regarding response strategies, the article proposes various suggestions, such as improving network security regulations and protection systems, enhancing public awareness and education, and promoting social cooperation. In particular, it emphasizes the importance of formulating and improving laws and regulations against online sexual violence, strengthening cross-departmental collaboration, enhancing digital literacy programs, and promoting gender equality through school education and media publicity to raise awareness and respect for women’s rights.

Research on Network Security Protection Technology Based on P2AEDR in New Low-Voltage Control Scenarios for Power IoT and Other Blockchain-Based IoT Architectures.

In the construction of new power systems, the traditional network security protection mainly based on boundary protection belongs to static defense and still relies mainly on manual processing in vulnerability repair, threat response, etc. It is difficult to adapt to the security protection needs in large-scale distributed new energy, third-party aggregation platforms, and flexible interaction scenarios with power grid enterprise systems. It is necessary to conduct research on dynamic security protection models for IoT and other Blockchain-based IoT architectures. This article proposes a network security comprehensive protection model P2AEDR based on different interaction modes of cloud-edge interaction and cloud-cloud interaction. Through continuous trust evaluation, dynamic access control, and other technologies, it strengthens the internal defense capabilities of power grid business, shifting from static protection as the core mode to a real-time intelligent perception and automated response mode, and ultimately achieving the goal of dynamic defense, meeting the security protection needs of large-scale controlled terminal access and third-party aggregation platforms. Meanwhile, this article proposes a dynamic trust evaluation algorithm based on deep learning, which protects the secure access and use of various resources in a more refined learning approach based on the interaction information monitored in the system. Through experimental verification of the dynamic trust evaluation algorithm, it is shown that the proposed model has good trust evaluation performance. Therefore, this research is beneficial for trustworthy Power IoT and other Blockchain-based IoT architectures.

Deep Learning-Based Network Security Situation Prediction for Sensor-Enabled Networks

Aims and background: Network security detection has become increasingly complex due to the proliferation of Internet nodes and the ever-changing nature of network architecture. To address this, a multi-layer feedforward neural-network has been employed to construct a model for security threat detection, which has enhanced network security protection. Objectives and Methods: Improving prediction accuracy and real-time performance, this research suggests an optimal strategy based on Clockwork Recurrent-Neural-Networks(CW-RNNs) to handle nonlinearity and temporal dynamics in network security circumstances. We get the model to pick up on both the short-term and long-term temporal aspects of network-security situations by using the clock-cycle RNN. To further improve the network security scenario prediction model, we tune the network hyperparameters using the Grey-Wolf-Optimization(GWO) technique. By incorporating a clock-cycle for hidden units, the model can improve its pattern recognition capabilities by learning short-term knowledge from high-frequency update modules and preserving long-term memory from low-frequency update modules. Results: The optimized clock-cycle RNN achieves better prediction accuracy than competing network models when it comes to extracting nonlinear and temporal characteristics of network security scenarios, according to the experimental data. Conclusions: In addition, our method is perfect for tracking massive amounts of data transmitted by sensor networks because of its minimal time complexity and outstanding real-time performance.

A network security protection scheme for tax system based on elliptic curve cryptography

With the rapid development of modern information technology and network technology, the construction of tax informatization plays an important role in improving the level of tax management and work efficiency. However, in the centralized data processing mode, the establishment of large data center and the efficient, stable, and secure connection of server bandwidth have become key challenges. Moreover, the openness and interconnectivity of the tax system network pose various network security threats, seriously threatening the integrity and security of data. For the above issues, this article proposes a tax system network security protection scheme based on elliptic curve cryptography. This scheme combines hash function and elliptic curve cryptography to achieve user anonymity and secure generation of session keys, effectively ensuring the secure transmission of confidential information. The security and effectiveness of the scheme are verified through informal and formal security analysis based on security proof, BAN logic and AVISPA tool. And a comprehensive evaluation of the scheme is conducted, and the results show that the scheme significantly reduced the costs while providing security features.

Event Based Control for Network Security Systems Subject to Actuator Saturation

Abstract As a typical nonlinear link, actuator saturation exists in most network security protection systems. It easily disrupts the closed-loop performance of the system, leading to instability. The event triggering control policy generally adopts an aperiodic sampling mechanism and transmits information only when the triggering condition is met, which can save system communication and computing resources. An event-triggered controller is designed for a network security system with actuator saturation, and the system stabilization is realized. The attractor domain of the system is estimated by solving optimization problems in the form of linear matrix inequalities. Simulation results show the validity of the conclusions obtained in this paper.

Research on Network Security and Privacy Protection Technology of Autonomous Vehicle

With the rapid development of information technology and the increasing maturity of intelligent transportation systems, autonomous vehicles, as an important carrier of future travel, are gradually moving from concept to commercial application. However, the highly connected and intelligent nature of autonomous vehicles also makes them face unprecedented cybersecurity and privacy protection challenges. This paper deeply discusses the security threats and privacy disclosure risks of autonomous vehicles in network architecture, data transmission, data processing and other links, and systematically reviews the current technical means and research progress to address these challenges. This paper Outlines the data collection and transmission mechanism of autonomous vehicles, including the collection and processing of sensor data as well as the communication process between vehicles and the cloud, other vehicles and transportation infrastructure, and analyzes in detail the network security threats faced by autonomous vehicles, including remote control attacks, malware attacks, communication hijacking, etc. These threats can lead to serious consequences such as vehicle failure, data breach, or system crash. At the same time, the paper also points out the challenges faced by autonomous vehicles in terms of privacy protection, such as the risk of leakage of sensitive data such as personal location information and driving habits, as well as the risk of data abuse and sharing. In order to address the above challenges, this paper focuses on the technical research progress of cybersecurity and privacy protection of autonomous vehicles. In the aspect of network security, this paper discusses the key technical means such as data encryption, access control, intrusion detection and response system, and emphasizes the importance of security hardware and software in improving the overall security of the system. In terms of privacy protection, this paper proposes strategies such as anonymization and desensitization technology, privacy protection protocols and standards to protect users' privacy from invasion.

Analysis of the Current Status of Patent for AI in Network Security Protection

Analysis of the Current Status of Patent for AI in Network Security Protection

Campus Network Traffic Prediction and Anomaly Detection Based on Deep Learning

This paper proposes an intelligent solution for network traffic prediction and anomaly detection in campus networks, addressing the increasingly severe network security challenges. The proposed approach innovatively integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM) to simultaneously extract local features and capture dynamic temporal dependencies of network traffic, significantly improving prediction accuracy. Based on this, an adaptive threshold anomaly detection algorithm is designed to automatically adjust detection sensitivity according to traffic variations, achieving a better balance between accuracy and recall rates. Additionally, an anomaly visualization scheme is presented, intuitively displaying the spatiotemporal distribution of network anomalies through heatmaps, assisting administrators in decision-making. Large-scale experiments demonstrate that this approach can effectively identify various security threats such as DDoS attacks, scanning probes, and botnets, with an overall detection rate exceeding 90% while maintaining a low false positive rate. Compared to traditional statistical and machine learning methods, the proposed approach exhibits stronger adaptability and generalization capabilities, providing crucial support for building an intelligent, precise, and reliable campus network security protection system. Future work will focus on further improving the real-time performance and robustness of the solution, expanding its application in new network scenarios such as IoT and edge computing.

Retraction Note: Research on campus network security protection system framework based on cloud data and intrusion detection algorithm

Retraction Note: Research on campus network security protection system framework based on cloud data and intrusion detection algorithm

Research on Power Cyber-Physical Cross-Domain Attack Paths Based on Graph Knowledge

Against the background of the construction of new power systems, power generation, transmission, distribution, and dispatching services are open to the outside world for interaction, and the accessibility of attack paths has been significantly enhanced. We are facing cyber-physical cross-domain attacks with the characteristics of strong targeting, high concealment, and cross-space threats. This paper proposes a quantitative analysis method for the influence of power cyber-physical cross-domain attack paths based on graph knowledge. First, a layered attack graph was constructed based on the cross-space and strong coupling characteristics of the power cyber-physical system business and the vertical architecture of network security protection focusing on border protection. The attack graph included cyber-physical cross-domain attacks, control master stations, measurement and control equipment failures, transient stable node disturbances, and other vertices, and achieved a comprehensive depiction of the attack path. Second, the out-degree, in-degree, vertex betweenness, etc., of each vertex in the attack graph were comprehensively considered to calculate the vertex vulnerability, and by defining the cyber-physical coupling degree and edge weights, the risk of each attack path was analyzed in detail. Finally, the IEEE RTS79 and RTS96 node systems were selected, and the impact of risk conduction on the cascading failures of the physical space system under typical attack paths was analyzed using examples, verifying the effectiveness of the proposed method.

A COMPREHENSIVE REVIEW OF ARTIFICIAL INTELLIGENCE APPLICATIONS IN ENHANCING CYBERSECURITY THREAT DETECTION AND RESPONSE MECHANISMS

This literature review explores the transformative impact of artificial intelligence (AI) on enhancing cybersecurity measures across various domains. The study systematically examines the integration of AI in Intrusion Detection Systems (IDS), malware detection, phishing detection, threat intelligence, network security, and endpoint protection. Key findings reveal that AI-driven techniques significantly outperform traditional methods, particularly in real-time threat detection, accuracy, and adaptive response capabilities. Network-based IDS benefit from supervised and unsupervised learning algorithms, improving the identification of malicious network traffic and novel attack patterns. In malware detection, AI-enhanced static and dynamic analysis methods surpass signature-based approaches by detecting previously unknown malware and complex behaviors. Phishing detection has seen substantial improvements with AI applications in email filtering and URL analysis, reducing phishing incidents despite challenges like false positives. AI's role in threat intelligence is critical, automating data analysis to uncover hidden threats and employing predictive analytics to anticipate and mitigate cyber attacks. AI techniques in network security and endpoint protection enhance real-time monitoring and authentication processes, providing robust defenses against cyber intrusions. Despite these advancements, challenges such as handling high data volumes and the need for continuous learning to adapt to emerging threats remain. This review underscores the significant advancements, practical implementations, and ongoing challenges of leveraging AI in cybersecurity, highlighting its potential to fortify digital defenses and address the complexities of contemporary cyber threats.

Research on Intelligent Cybersecurity Protection System in Cloud Computing Environment

In the digital era, cloud computing has transformed data processing, storage, and analysis with its scalability and flexibility. However, it also introduces new security challenges, such as data breaches and unauthorized access, which are exacerbated by Advanced Persistent Threats (APT) and zero-day vulnerabilities. This study aims to construct an intelligent network security protection system that adapts to the dynamic cloud computing environment, leveraging artificial intelligence and machine learning to enhance security efficiency and accuracy. The research objectives include developing a system that maintains privacy protection and user experience while ensuring secure access to cloud services. The paper presents a multi-layered architecture for the intelligent cybersecurity protection system, incorporating data collection, threat detection, access control, and adaptive security policies. Experimental methods and results validate the system’s effectiveness, demonstrating improved detection rates and reduced false positives compared to existing solutions. The study concludes with a discussion on the model’s advantages, limitations, and future application prospects in the field of cloud computing security.

Design of network security monitoring system based on CNN and exponential weighted D-S evidence theory

ABSTRACT To address the poor network security protection and low attack traffic identification, the study designs a network security monitoring system based on convolutional neural network and exponential weighted Dempster Shafer evidence theory. The validation of the UNSW-NB15 data set showed that the output of multi-source fusion after exponential weighted Dempster Shafer evidence theory was higher than the output of the feature fusion by 3.92%. The accuracy of the attack recognition was as high as 93.72%, which was 1.85% higher than feature fusion. The accuracy of the proposed network security monitoring system increased by 3.70% on average over other methods. The results indicate that the proposed network security monitoring system can effectively improve the efficiency of network attack identification, monitor network security in real-time, and effectively protect network operation. The system is feasible and reasonable in terms of network security situational awareness, which can provide effective situational analysis for network administrators.

Analysis and Application of Chaotic Genetic Algorithm Based on Network Security in The Research of Resilience of Cluster Networks

With the wide application of UAV, in the actual flight process, UAV needs to calculate the safe path according to its own position, environment, obstacles and other information. Due to the complex and changeable scene and environment of UAV mission execution, it is very important to select an appropriate UAV path planning algorithm. This paper aims at the path planning problem of multiple UAVs in a complex three-dimensional environment to ensure that multiple UAVs reach the mission location from different angles. Taking the chaotic genetic algorithm in network security protection as the main body, the operation difficulty of the algorithm is reduced, and the solution speed and accuracy of the algorithm are improved. The path length obtained by the proposed algorithm is 8.4% less than that of the ABC algorithm, 11.3% less than that of the PSO algorithm, and 4.2% less than that of the BABC algorithm. The system running time of the improved algorithm is also reduced by 27% to 45% compared with other algorithms. In terms of unmanned cooperation, this paper proposes a system capability based on network modeling to improve the cooperative combat capability of multiple UAVs. By establishing a network model, information sharing, collaborative decision-making and collaborative decision-making between drones are realized, thereby improving the effectiveness of the entire system. At the same time, this paper also considers the problem of network survivability. By introducing redundant design and fault recovery mechanism, the robustness and reliability of the system are enhanced.

Enhanced Network Security Protection through Data Analysis and Machine Learning: An Application of GraphSAGE for Anomaly Detection and Operational Intelligence

With the Internet's rapid expansion, network security challenges have become increasingly complex and prominent. Traditional protection methods, largely dependent on predefined rules and patterns, demonstrate limited effectiveness against sophisticated and unknown network attacks, failing to harness the full potential of extensive network data. This study addresses the challenges faced by modern cybersecurity, particularly the limitations of traditional defense methods in countering unknown and complex attacks, by proposing a solution that integrates data analysis and machine learning technologies. The focus of this research is placed on network security anomaly detection as well as on intelligent network operations and maintenance exception management based on graph network algorithms, aiming to enhance security defense capabilities and operational efficiency. Specifically, the main contributions and innovations of this paper include: 1. Innovations in sampling, aggregation, and loss functions within the Graph Sample and Aggregation (GraphSAGE) model to improve the accuracy and robustness of the model for network anomaly detection; 2. The introduction of a novel network anomaly root cause analysis and localization model, which, combined with an optimized root cause likelihood assessment method and search scheme, significantly enhances the speed and accuracy of anomaly localization; 3. The design of an integrated decision support system that can automatically adjust protection strategies as network conditions change, achieving a high level of automation and intelligence in cybersecurity management. This work not only provides effective technical support for network security protection but also opens new avenues for future cybersecurity research.

Control modeling and optimization of network information security system based on deep learning data interaction

In order to solve the problem of poor network security protection performance of traditional network information security protection systems, the author proposes a network information security system control modeling based on deep learning data interaction. Adopt CY8C2453 type processor to improve system data exchange and data processing speed, enhance system stability through peripheral backplane, and evaluate network information security, at the same time, the authority management mechanism is set, and the general strategy form of the network attack and defense game is defined as a triple, calculate the attacked path in a certain network area and the defense strategy that the defender should choose, so as to complete the network information security protection based on the attack-defense game model. Experimental results show that: The number of attacked information of the system is 58 less than that of the traditional system; In the webpage attack, the number of attacked information of the designed system is 82 less than that of the traditional system; In the data interception attack, the number of attacked information of the designed system is 77 less than that of the traditional system. ConclusionUnder different attack behaviors, the number of successful attacks on the network information security protection system based on the attack-defense game model designed this time is less than the number of successful attacks on the traditional system, which improves the security of network information.

Retraction Note: The design of network security protection trust management system based on an improved hidden Markov model

This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1186/s13635-023-00146-z.

An Adaptive Enhancement Method of Malicious Traffic Samples Based on DCGAN-ResNet System

A malicious traffic sample adaptive enhancement device based on Deep Convolutional Generative Adversarial Network (DCGAN) is designed to address the issue of imbalanced network traffic data distribution, aiming to enhance the accuracy and efficiency of anomaly detection. By leveraging generative adversarial network technology, this device can generate samples similar to real malicious traffic to balance the training dataset. It utilizes the generator and discriminator of the Deep Convolutional Generative Adversarial Network (DCGAN), combined with the residual network (ResNet) in the CNN model, to enhance the quality of generated samples. The device can switch states to adapt to various network environments and has been experimentally validated for its effectiveness and feasibility.Moreover, employing an adaptive device, the samples of malicious traffic are adjusted. Experimental analysis demonstrates that the device significantly enhances the accuracy of anomaly traffic detection, improves robustness, and provides robust support for network security protection.