Network Security Problems Research Articles

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.

WITHDRAWN: Design of Group Key Agreement Method Based on Elliptic Curve Cryptography in New Energy Extensive Access Security Protection

Aiming at the security problem of power system communication network under the condition of extensive access of new energy, we design the group key agreement method based on elliptic curve cryptography in the security protection of new energy extensive access. Design the architecture of power system security protection network under the condition of extensive access to new energy. The network architecture adopts wireless communication technology to realize the transmission of new energy security monitoring information. For the data confidentiality problem in the wire communication technology, the group key agreement method based on elliptic curve cryptography is proposed, and the group key agreement model is constructed under the set conditions, and the group key is generated through the processes of main parameter selection, group composite public key distribution, group key agreement protocol design, group key update protocol for node joining event, and group key update protocol for members departure event, etc. The group key is utilized for the communication. The experimental results show that this method can generate the key with high randomness and excellent key sensitivity, which can effectively resist the attacks of character frequency statistics and information entropy and guarantee the security of communication information.

Reflective Distributed Denial of Service Detection: A Novel Model Utilizing Binary Particle Swarm Optimization-Simulated Annealing for Feature Selection and Gray Wolf Optimization-Optimized LightGBM Algorithm.

The fast growth of the Internet has made network security problems more noticeable, so intrusion detection systems (IDSs) have become a crucial tool for maintaining network security. IDSs guarantee the normal operation of the network by tracking network traffic and spotting possible assaults, thereby safeguarding data security. However, traditional intrusion detection methods encounter several issues such as low detection efficiency and prolonged detection time when dealing with massive and high-dimensional data. Therefore, feature selection (FS) is particularly important in IDSs. By selecting the most representative features, it can not only improve the detection accuracy but also significantly reduce the computational complexity and attack detection time. This work proposes a new FS approach, BPSO-SA, that is based on the Binary Particle Swarm Optimization (BPSO) and Simulated Annealing (SA) algorithms. It combines these with the Gray Wolf Optimization (GWO) algorithm to optimize the LightGBM model, thereby building a new type of reflective Distributed Denial of Service (DDoS) attack detection model. The BPSO-SA algorithm enhances the global search capability of Particle Swarm Optimization (PSO) using the SA mechanism and effectively screens out the optimal feature subset; the GWO algorithm optimizes the hyperparameters of LightGBM by simulating the group hunting behavior of gray wolves to enhance the detection performance of the model. While showing great resilience and generalizing power, the experimental results show that the proposed reflective DDoS attack detection model surpasses conventional methods in terms of detection accuracy, precision, recall, F1-score, and prediction time.

Research on Optimization of Communication Network Security and Intelligent Supervision Model in Urban Community Aging Adaptation Renovation

In the process of increasing the number of elderly people, their care and attention have received greater attention, which has made traditional community environments face insufficient data encryption performance and data mining services in the communication network security of aging communities. This has made communication security optimization and intelligent monitoring models in urban community aging adaptation and transformation a focus of research in the field of data communication security. This study delves into the improvement and enhancement of intelligent monitoring models for communication network security in urban communities. Its goal is to improve the security and intelligent monitoring mode of data communication networks during the transformation process through technological innovation and progress in management practices, and further improve the security performance of data communication. This research has solved the communication network security problems existing in intelligent devices and Internet services in the community aging transformation. Through the analysis of improvement strategies, this article compares the basic performance indicators of data encryption, firewall performance, data analysis and mining, and intelligent recommendation in communication network security and intelligent recommendation between different models. The optimization of communication network security in urban community aging adaptation can ensure the data security of terminal devices and further improve and enhance intelligent recommendation services for aging, providing more references and suggestions for communication network security and intelligent monitoring.

Big data algorithm for computer network information security in the 5G era

Computers play a very important role in today’s life. A large amount of personal information exists in computers, so it is very important to protect the information security on computer networks. The development of information technology has brought a huge impact on human society, and the information security of its computer network has attracted more and more attention. Aiming at the needs of computer network information management in the age of big data, this paper has developed a special computer network information protection system to deal with various threats and constantly improve the protection system. Based on this, the concept and advantages of the 5G network are first expounded, and the management technology of the 5G network is briefly analyzed. Then the information security problems of computer networks in the 5G era are considered, and effective solutions to the information security problems are proposed. Through research and calculation, the new computer network security system can improve network information security by 13.4%.

Thinking and Research on the Construction of Web Application Firewall in Smart Campus Environment

With the rapid development of information technology, the Internet has penetrated into various fields, especially the field of education. As an important carrier of education informatization, smart campus provides strong support for improving education quality and management level. However, the network security problems of smart campus also come up, especially the security problems of Web applications. Web Application Firewall, as an important network security technology, plays a vital role in the construction of smart campus, and is of great significance for guaranteeing the information security of smart campus. This paper mainly discusses how to effectively build a Web Application Firewall in a smart campus environment, including the design, configuration, and management of the firewall, in order to safeguard the security of the smart campus network.

An Intrusion Detection Model Based on Extra Trees Algorithm with Dimensionality Reduction and Oversampling

With the advancement of the university information process, more and more application systems are running on the campus network, and the information system becomes larger and more complex. With the rapid growth of network users and the popularization and deepening of computer knowledge, the campus network has been transformed from an experimental network for education and scientific research into an operational network that attaches equal importance to education, scientific research, and service. As the most important transmission carrier of digital information, how to ensure its security has become an urgent issue for colleges and universities. Therefore, this paper uses advanced intrusion detection technology to design the corresponding model to solve the security problem of the campus network. When traditional machine learning algorithms train network intrusion data sets, they are prone to problems such as too many feature dimensions, overfitting, and imbalance of data sets, which lead to lower accuracy and low time efficiency of intrusion detection algorithms. In order to solve the above problems, this paper proposes an intrusion detection model based on Extra Trees, which uses linear discriminant analysis to reduce the dimension of data, then uses oversampling to reduce the influence of imbalance of sample categories of the network intrusion dataset, and finally uses Extra Trees algorithm to train the model. The experimental results show that after LDA reduction and oversampling, using the Extra Trees classification model can improve the overall recognition performance of imbalanced data sets under multi-classification and satisfy the network intrusion detection.

Critical Infrastructures at Risk Addressing Information Security in Power Systems

According to the power system network security risks and problems, this paper presents the work of information security and the general principles of information security from a technical and safety management put forward the idea of addressing security issues and methods of analysis of local area network security management related to issues, and according to their own experience of a relatively effective way of thinking.

Using Deep Learning Technology to Optimize VPN Networks Based on Security Performance

In recent years, network developments and user growth have increased network security problems and techniques. The trend in network security is towards web-based networks, given Internet users' diverse origins, unpredictable persons are more likely to participate in malevolent activities. Security and privacy safeguards are implemented using many technologies. This paper proposes using a virtual private network (VPN) to secure particular communications across vast networks. VPNs restrict unauthorised connections, benefiting secured hosts. Through a VPN network, connections can be kept hidden and external connections prohibited. The influence of a virtual private network (VPN) on a standard network's performance is studied by producing and assessing CBR, HTTP, and FTP payloads. The evaluation used throughput and time delay as performance measures after analysis of the finding, deep learning (DL) can predict attacks. Because that learns attack patterns during training to effectively forecast attacks. To detect attacks, deep learning-based attack prevention model was created This method uses Nave Bayes and FFNN to enhace network performance. The results show that VPNs affect packet latency and performance differently depending on the data type. The FFNN algorithm detects intrusions with 98% accuracy.

Security and privacy protection protocol based on edge computing in smart campus

AbstractWith the continuous emergence of modern Internet of Things, mobile Internet and other new generation of campus information technology and its extensive application in professional campus education, the development of college professional campus education information construction has entered a new stage of development. In view of higher requirements on data storage and network transmission delay of core business system in smart campus and more prominent wireless network security problems, this paper proposes a privacy protection protocol for smart campus based on chaotic mapping and edge computing. The protocol uses chaos mapping algorithm to protect user identity and negotiate session keys, and introduces edge computing to process authentication information on the edge server, which reduces the pressure of the core network and improves the authentication efficiency. This paper proves the security of the protocol by using BAN logic and Scyther, and verifies that the protocol can resist known attacks by analysing informal security. Through performance analysis and comparison, the proposed protocol has obvious advantages in computation and communication overhead.

Evaluation on the design of embedded platform vision system under the background of artificial intelligence

At present, the application of Artificial Intelligence (AI) in industrial control, smart home and other fields has received good response. However, AI technology has certain requirements for computer performance, and also faces problems in network security, data analysis, human-computer interaction, etc. At present, the visual platform of embedded system has achieved remarkable results in practical applications, but its development has been seriously hampered by problems such as low overall development efficiency and unstable system performance. This paper designed an EP Vision System (VS) based on AI technology. The platform combined the embedded hardware design with the Support Vector Machine (SVM) algorithm to realize the intelligent robot interaction and target detection functions. The test results showed that when other conditions were the same, students and experts had 83.5% and 90% positive evaluations of System X, and 16.5% and 10% negative evaluations respectively. However, their positive evaluation of System Y only accounted for 19% and 4%, while the negative evaluation accounted for 81% and 96%. The proportion of positive evaluation of System X was much higher than that of System Y, which indicated that System X can meet the actual application requirements and improve the system recognition efficiency to a certain extent. It showed the positive relationship between AI technology and EP VS.

Realistic Dilemma and Path Optimization of Online Personal Information Protection

A virtual area made by computers is called cyberspace. People there lead non-traditional lifestyles. It has evolved into a different manner of living from the physical realms of land, sea, air, and space. Lifestyle changes have also led to the emergence of some network security problems, including invasion of privacy, identity theft, financial fraud, and more. In today's world, cybersecurity is not only a trendy topic but also a crucial issue to protect the interests of cyberspace's development, security, and sovereignty. The "Personal Information Protection Law of the People's Republic of China" offers numerous highlights in terms of fundamental principles, scope of adjustment, and protection models, and offers an institutional response to the main challenges faced by personal information protection in the big data era. To strike a balance between information protection and information freedom, the Internet industry must continue to bolster industry self-discipline, raise public awareness of the right to protect personal privacy information and enhance public understanding of the legal protection of personal privacy information rights.

Intrusion detection method based on improved social network search algorithm

The network security problem in today's world is becoming more and more prominent, and intrusion detection as a branch in the field of network security has been developed tremendously. At present, back propagation (BP) neural network is widely used in intrusion detection. However, its weights and thresholds are randomly initialized, so that fall into local optimal after training. To solve this problem, an intrusion detection model based on improved social network search (ISNS) algorithm to optimize BP neural network is proposed. The model first uses chaotic mapping and elite mechanism to improve the original Social Network Search (SNS) algorithm, and then uses the ISNS algorithm to filter the initial weights and thresholds of the BP neural network, and constructs the neural network with the optimal values to avoid falling into local optimum. The experimental results show that the proposed method solves the problem that the traditional BP neural network is easy to fall into local optimum. At the same time, the ISNS_BP model achieves better classification performance than other models, and the accuracy on the NSL-KDD and UNSW-NB15 datasets reaches 98.62% and 93.97%, respectively.

Improved K-means-based solution for detecting DDoS attacks in SDN

Software Defined Networking (SDN) is a change to the traditional network, which can better face the complex network scenarios and meet the future network development needs. However, SDN faces many network security problems in the process of development, among which distributed denial-of-service (DDoS) attacks on SDN cause the most serious problem of communication paralysis. For the problem of unstable detection accuracy based on K-means algorithm detection scheme, a detection scheme based on density selection and mutual information improvement K-means algorithm (MIK-means) is proposed, which can well improve the stability and accuracy of the detection effect. Through a large number of experimental results show that the detection scheme maintains an average accuracy of more than 96% under different attack types and rates, while the detection scheme also maintains 5 6 s from response to completion of mitigation, which well reduces the computational overhead.

Improved RFID mutual authentication protocol against exhaustive attack in the context of big data

The development of big data has epromoted the development of Internet technology, but it has brought more network security and privacy problems. Therefore, how to solve network security problems is the main research direction of current network technology development. In order to deal with the harm of network attacks to personal privacy security, this paper studies and proposes an RFID mutual authentication protocol against exhaustive attacks based on improved Hash function, and proposes a security proof based on BAN logic rules. At the same time, to enhance the computing resources of the improved protocol, this paper proposes an improved authentication query protocol for multi-source RFID tags. In the performance analysis, when the distance between the reader and the tag reaches 10 m, the improved protocol can still be higher than 90%. The application test shows that the improved protocol proposed in the study is capable of resisting exhaustive attacks, its execution time is short, and it is less affected by the number of tags. The above results show that in the context of big data, the improved RFID mutual authentication protocol proposed by the research against network exhaustive attacks has a more significant defense effect, can effectively protect user privacy, and has a greater reference value in network security research.

Stealthy FDI attacks on modified Kalman filtering in complex networks with non-Gaussian-Lévy noise

This paper investigates the problem of network security for stealthy false data injection (FDI) attacks under modified Kalman filtering (MKF) over complex networks with non-Gaussian-Lévy noise (NGLN). Initially, a modified Kalman filter (MKFR) is proposed to estimate system states, where the estimation function is related to the probabilistic characteristics of Lévy noise, and the saturation threshold is designed in relation to Lévy noise parameters. The upper bound of error covariance is obtained, and the boundedness of the upper bound is proven through the use of mathematical induction and iterative methods. Second, based on the MKF, this paper proposes a two-channel stealthy FDI attacks (TSFAs) strategy that is related only to the system model, which is injected into the sensor-to-controller (S-C) and controller-to-actuator (C-A) transmission channels at the same time. In addition, the attacker sets two MKFRs as observers to estimate the states of the target system, which are used to adjust the attack signal. Third, a sufficient condition is obtained that demonstrates the stability of the system. Meanwhile, TSFAs can avoid being detected by the residual-based detector to guarantee stealthiness. Finally, the effectiveness of the MKF is verified by the numerical simulation, and the stealthiness of the TSFAs and the impact on the system stability are also demonstrated.

ИССЛЕДОВАНИЕ СТРАТЕГИЙ БЕЗОПАСНОСТИ ИНФОРМАЦИОННЫХ ТЕХНОЛОГИЙ КОМПЬЮТЕРНЫХ СЕТЕЙ

The improvement of computer network technologies has brought convenience to people’s lives. With the growing popularity of Internet users, more attention has been paid to the security of computer networks. The security problems of computer networks are mainly related to the strong development of computer networks, the presence of insecurity and the possibility of disclosure of user information, the occurrence of leaks, intrusion risks and other phenomena. Therefore, we must pay attention to information management of computer networks, strengthen protection and ensure the safety of users during use.

Analysis of Financial Data Risk and Network Information Security by Blockchain Technology and Edge Computing

To solve the problem of computer network security, this study creatively establishes a new information security (IS) strategy recommendation system and a cooperative intrusion detection system (CIDS) based on blockchain (BC) and edge computing (EC) technologies. The innovation lies in the proposal of the real-time CIDS method. First, the related technologies and theories of the Bayesian network, deep learning (DL), and neural network (NN) are analyzed. Second, a real-time IS strategy recommendation system is constructed using NNs and expert systems, and CIDS is built on BC and DL networks. Finally, experiments compare recurrent NN and long-short term memory (LSTM). The research results reveal that the accuracy of LSTM can reach about 93%. The comparison between the single intrusion detection system (SIDS) and CIDS denotes that CIDS is better than SIDS in detection accuracy, and the specific performance is 97.54% > 91%. The proposed model based on business continuity management can help enterprises better protect financial data and establish a complete protection system of network information security (NIS). In this study, a CIDS with good performance is obtained, which lays a foundation for the further development of the NIS.

Improved network intrusion classification with attention-assisted bidirectional LSTM and optimized sparse contractive autoencoders

Accurately identifying network intrusion cannot only help individuals and enterprises better deal with network security problems, but also maintain the Internet environment. This work proposes a new hybrid classification method named SABD for network intrusion detection. SABD integrates Stacked sparse contractive autoencoders (SSCA), Attention-based Bidirectional long-term and short-term memory (LSTM), and Decision fusion. Specifically, SSCA is used for extracting features, which are sent to the attention-based bidirectional LSTM for the classification. Besides, an improved optimization algorithm named genetic simulated-annealing-based particle swarm optimization is designed to optimize hyperparameters of SSCA. Finally, the decision fusion algorithm is adopted to integrate classification results of multiple classifiers and yield the final results. Based on experimental results from four different types of data sets, the proposed SABD outperforms its most advanced peers in classification accuracy.

Applications of Nonlinear Analysis Transforming Communication Paradigms for Seamless Connectivity

The dynamic nature of communication paradigms demands uninterrupted connectivity in order to achieve maximum functionality. It will take creative thinking to achieve this smoothness. It becomes clear that nonlinear analysis is a revolutionary methodology that provides original insights and solutions. In order to attain seamless connectedness, this study investigates how nonlinear analysis might be applied to revolutionise communication paradigms. A strong foundation for comprehending and improving communication systems is provided by nonlinear analysis, which has its roots in the study of complex systems. This paper delves into the fundamental concepts of nonlinear analysis and elucidates how they may be applied to the complicated dynamics of modern communication. Nonlinear analysis, which includes information theory, signal processing, and chaos theory, introduces a new paradigm in communication technology. Network congestion, signal distortion, and security holes are just a few of the issues plaguing today's communication infrastructure that cause disruptions in service. Nonlinear analysis is a useful technique for overcoming and reducing these challenges. The efficiency, dependability, and security of communication networks can all be improved by implementing nonlinear system dynamics. In this study, nonlinear analysis is applied to real-world problems in network security, wireless communication, and the Internet of Things (IoT) through several detailed case studies. Using these real-world examples, we can see how nonlinear methods can successfully alter established modes of communication and demonstrate their value. The implications of nonlinear analysis for future communication paradigms are far-reaching and extend beyond the capabilities of current technology. This essay delves into the future of communication technology, shedding light on emerging trends that may have profound impacts in the years ahead. It also examines the benefits and difficulties that come with employing nonlinear analysis to establish seamless communication. Nonlinear analysis is a potent instrument with potential applications in shifting communication paradigms, facilitating seamless connectivity, and paving the way for the creation of novel forms of communication technology. The study emphasises the significance of nonlinear techniques in addressing the challenges of modern communication systems and guiding them towards a more connected and successful future.