Network Security Protection Research Articles

RETRACTED ARTICLE: The abnormal precipitation in rainy season based on genetic algorithm and the security of big data in network

In this paper, cloudsim cloud simulation tool is studied, and its running environment is reasonably configured. Taking the platform as the background, the improved genetic algorithm proposed in this paper is used for simulation experiment that is to optimize the genetic algorithm through simulated annealing behavior. Through the simulation experiment of the improved genetic algorithm, it can be known that the genetic algorithm designed in this paper can satisfy multiple customers one by one and has a certain improvement effect on cloud service quality. In this paper, the problem of rainfall anomaly in city D is studied. The results show that the rainfall anomaly of city D is related to solar activity, 500hPa height field in winter and SST in the Central South Pacific from June to August of the previous year. It is found that there is almost no rainfall during the high solar activity years, while there is frequent rainfall near the low solar activity years. Finally, according to the homomorphic encryption mechanism, this paper studies the protection of data confidentiality in computer network security and the multistorage technology of big data. In this research scheme, by adding a certain number of virtual objects to two different computing information dummies, we successfully protect the data such as the number of items owned by the data owner, the content of the items owned, and the item objects called by the user. In addition, this paper uses bilinear mapping and RS coding technology to create related protocols to solve some problems, such as infinite verification of big data integrity, error data recovery, and protection of data owner’s computing resources. Through the design of solutions to the above problems, this paper successfully completed the protection of big data resources and network security based on cloud computing environment.

Network Security Protection Technology under the Background of Computing Big Data

Based on the risk analysis of the network security of the radio and television critical information infrastructure, this paper introduces the evolution process of the network security protection architecture, and proposes the network security intelligent protection architecture. The article starts from the protection content and methods of physical security, network security, border security, server security, terminal security, application security, data security and backup and recovery, security management center and other technical aspects. Construction is explored. The thesis builds a reliable and stable broadcast technology center network information security system through the establishment of a network information security technology guarantee and a network information security management system.

Application of Virtual Private Network Technology in University Network Information Security

With the rapid progress of network technology, people gradually increase the coverage of the network. Network technology integration in all aspects of people’s lives. Information has become everyone’s important property. Many schools begin to pay attention to information security. In this process, people found some main measures of network security protection[5]. The application of campus security information protection based on virtual private network technology has become a hot topic of experts. This paper describes the characteristics of virtual private network. Finally, this paper puts forward some suggestions on campus information protection.

An empirical study on vulnerability assessment and penetration detection for highly sensitive networks

Abstract With the advancement of internet and the emergence of network globalization, security has always been a major concern. During the trial operation, the management control platform discussed in this article included more than 600 network security vulnerabilities in the industry, with dozens of incidents, which were promptly dealt with and rectified, effectively improving the level of network security management and protection in the industry. As networks are very much vulnerable to denial of service attacks, much more emphasis has been given to security. By improving their network security, network administrators have often tried their best. To attempt penetration testing, it is the best way of ensuring the system security. With the development of information technology, the security requirement of information system is increasing day by day. The use of penetration testing technology is conducive to the realization of accurate positioning, accurate detection, and active alarm of security vulnerabilities, and the optimization of monitoring and rectification of the combination of network security management control system. Taking penetration testing technology as one of the core elements of management and control, the risk index model is optimized to make network security management controllable and efficient, and effectively achieve management and control objectives.

Construction and Application of Machine Learning Model in Network Intrusion Detection

The modeling of network intrusion detection is a kind of significant network security protection technology. Nowadays, the network intrusion detection model can not accurately describe the intrusion behavior, so that incomplete network intrusion detection will occur. Therefore, based on machine learning algorithm, a network intrusion detection model is designed in the paper, which supports the fact that vector machine (SVM) fits the mapping relationship between network intrusion detection characteristics and network intrusion behavior. Meanwhile, a network intrusion detection model that reflects the relationship between the two aspects is established in the paper too. As a result, the experimental results show that the model proposed in the paper can not only accurately identify the network intrusion behavior, but has a very fast detection speed. Moreover, the model proposed in the paper has obtained much better network intrusion detection results than that of other models, which is of wide application prospect.

Research on Information Encryption Algorithm under the Power Network Communication Security Model

With the continuous development of current high-tech, network communication is quickly popularized in power automation monitoring systems, and colleagues have also brought many hidden dangers in communication security. This paper proposes a model that adapts to the security of power network communication. Based on the information encryption algorithm, this model can fully guarantee the security of both ends of the data transmission and can be applied to other network communication modes. Based on the power network communication security model, this article can use high-strength information encryption algorithms to encrypt data in the network to prevent the data transmitted in the network from being stolen or modified. The power network communication security model studied in this article mainly includes information encryption and decryption algorithms, client environment, and an SSL-based information encryption transmission protocol. Through analysis, it can be understood that the encryption and decryption algorithm is to perform MD5 transformation on key data and key strings, and the other is to use DES as the encryption algorithm before transmission. The experimental research results show that in the construction of power network communication, the power communication network is one of the most basic facilities in the development of the power grid. It is necessary to ensure the safety and economic operation of the power grid, and to improve the information level of the power grid enterprises and the network security protection system.

Information Hiding Technology under the Background of Power System Network Security

Power grid plays an important role in the development of the country and people’s life. It can be said that both production and life are inseparable from the power grid. Therefore, when the power system network security threats, the whole life will be adversely affected. Traditional power system network security protection measures have been unable to meet the current demand of power system network security protection. Information hiding technology is an advanced network information hiding technology, which has been widely used in recent years. Therefore, this paper puts forward the research of information hiding technology under the background of power system network security. In this paper, the power system network and information hiding technology are deeply studied. The analysis shows that the latest information hiding technology can play a good role in security protection of power system network, especially for various camouflage attacks. In this paper, according to the actual security requirements of power system network, combined with the main characteristics of information hiding technology, the traditional information hiding algorithm is optimized and improved. In the verification experiment, compared with the traditional algorithm, the NC value of the optimization algorithm in this paper has been significantly improved. The NC value of the traditional algorithm of 5x5 Gaussian low-pass filters is 0.619, while the algorithm of this paper reaches 0.869, the effect is obviously improved. Analysis shows that the algorithm in this paper further improves the NC value of the traditional algorithm, and improves the robustness while ensuring the image quality.

Research on Network Traffic Anomaly Detection Method Based on Deep Learning

Network attacks are a serious problem in today’s information society. Intrusion detection systems are an important guarantee for network security. The core of IDS is the intrusion detection algorithm. Under the current new situation of network security protection, traditional signature-based misuse detection algorithms cannot solve the problem of rapidly changing forms of network attacks. Machine learning and data mining methods have been widely used in attack detection in the network environment to improve the detection rate. Based on the in-depth analysis and research of deep learning models and intrusion detection methods, this paper proposes a convolutional neural network abnormal traffic detection model based on dynamic adaptive pooling for the pooling layer can only use one fixed method for down sampling, and proposed an abnormal traffic detection framework and a dynamic adaptive CNN model. Experimental analysis shows that the method proposed in this paper shows better detection accuracy and loss value in traffic detection.

Gated recurrent unit-based parallel network traffic anomaly detection using subagging ensembles

Recently, wireless network evolution has been primarily driven by a need for higher rates. The ongoing deployment of 5G cellular systems is continuously exposing the inherent limitations of this system, which promote the exploration of 6th generation mobile networks (6G). However, development is bound to be challenging. The complex network environment, rapidly growing data volume and new types of network attacks and anomalies will become an obstacle to network security protection. To solve these problems, we propose a novel parallel subagging-GRU-based network traffic anomaly detection method (PSB-GRU) for identifying anomalies in the network. Considering the advantages of gated recurrent unit (GRU) self-learning and long-term dependency processing, we use it as the main structure of anomaly detection, and we use a genetic algorithm (GA) to realize the intelligentization of its training process. In addition, we introduce the Spark platform to parallelize the detection process and improve detection efficiency. Additionally, to reduce the variance and mean square error in all order terms and improve the generalization ability of the detection model, we utilize a subagging algorithm to reinforce the detection model. Finally, we compare our anomaly detection method with some existing algorithms and show that the anomaly detection performance of the proposed method is better than that of the recurrent neural network methods (RNN, LSTM and GRU).

Analysis of Network Intrusion Detection Technology Based on Computer Information Security Technology

Data is the basis of information technology security management, with the arrival of the era of big data, computer network security is also facing a lot of challenges, we all know that the network has many characteristics: open, virtual characteristics, which there are a lot of security risks. So we must strengthen the network security analysis, explore the main network security hidden danger, and put forward the corresponding to the computer information network security precautions, now is the information age, big data is the basic characteristic, and the Internet technology widespread application, computer network security problems often appear, often have all kinds of viruses into our computer, so we also want to strengthen the network security management, and puts forward the countermeasures, so as to improve the level of safety in network, strengthen network security protection, it is very important[1].

Industrial Control Trusted Computing Platform for Power Monitoring System

The traditional network security protection methods are not enough to cope with the increasingly harsh network environment. It is of great significance to construct the active defense system of power monitoring network through trusted computing technology. Based on trusted computing technology, this paper studies the trusted computing platform for power monitoring system, mainly studies the trusted computing platform of master station and the trusted enhancement of field measurement and control equipment. Trusted platform of master station and trusted enhancement of field measurement and control.

Research on Network Security Protection of Application-Oriented Supercomputing Center Based on Multi-Level Defense and Moderate Principle

High Performance Computing (HPC), also known as Supercomputing, is widely applied in various fields, which have the advantage of high processing capability and mass storage. Various open business services bring great threat to the network and data security of HPC cluster. This paper proposes a feasible scheme to realize supercomputing information security through multi-layer defense and overheads control according to the experience of operation, management and maintenance of Supercomputer Center for many years. First, this paper puts forward the principle of moderate risk, and gives the principles of design and the implementation for the security protection of Supercomputing Center. Second, this paper presents a security system architecture in coordination with deep collaborative defense. Finally, this paper designs a 4-layer protection scheme on account of the latest network security solutions and the operation and maintenance characteristic of our own supercomputer. Experiments on multi-level defense and overheads control show that the designed scheme takes into account the cost and network security, and obtain prospective results., which is especially suitable for the network security protection of small-scale or medium-scale high-performance computing cluster for engineering application.

Research on network security risk analysis method based on full traffic

In the application process of industrial Internet, network security threats and malicious attacks occur from time to time. In order to improve the ability of network perception of unknown threats and the level of network security protection, this paper proposes a network security detection method based on full flow. Through online intelligent detection of network malicious traffic, unknown threats and malicious attacks can be found, provide guarantee for improving enterprise network security level.

Full‐scene network security protection system based on ubiquitous power Internet of things

SummaryPower Internet of things network technology has a “double‐edged sword” effect; on one hand, let us feel fast and convenient, but on the other hand, we are wandering in the network security. At the same time of paying more attention to the quality of human life, it is also necessary to take into account the network security issues related to the power Internet of things. Therefore, this paper aims to study the full‐scene network security protection system based on ubiquitous power Internet of things, which is an important topic at present. This research investigates the effectiveness of the Internet of Things in the network security protection system in general scenarios by investigating the traditional network security risk index and analyzing the excellent performance of the Internet of Things, aiming at the security risks of traditional network servers. In actual application scenarios, the Internet of Things can protect the entire network system in an all‐round way anytime and anywhere. This powerful defense performance is unmatched by traditional networks. Big data intelligent monitoring system is used to monitor the actual operation of all‐weather and all‐scene network protection system. Experimental data show that in the ubiquitous power Internet of things environment, although there are still some risks in the operation of grid companies and other intelligent terminals, the number of security risks is significantly lower than that of the traditional Internet of things. The significance of “ubiquitous” power Internet of things is the all‐scene network security protection system that works all day and all the time. Integrated servers can better protect against security risks than single servers. Experimental data shows that the full‐scenario network security protection system in the Internet of Things effectively integrates the facility resources in the power system and the communication system, and it improves the company’s perception of the entire actual scene during the operation. At the same time, it also promotes the integration of corporate information and provides intelligent support for corporate decision‐making.

Network Security Management and Protection of Industrial Internet Equipment

Network Security Management and Protection of Industrial Internet Equipment

An Exhaustive Research on the Application of Intrusion Detection Technology in Computer Network Security in Sensor Networks

Intrusion detection is crucial in computer network security issues; therefore, this work is aimed at maximizing network security protection and its improvement by proposing various preventive techniques. Outlier detection and semisupervised clustering algorithms based on shared nearest neighbors are proposed in this work to address intrusion detection by converting it into a problem of mining outliers using the network behavior dataset. The algorithm uses shared nearest neighbors as similarity, judges whether it is an outlier according to the number of nearest neighbors of a data point, and performs semisupervised clustering on the dataset where outliers are deleted. In the process of semisupervised clustering, vast prior knowledge is added, and the dataset is clustered according to the principle of graph segmentation. The novelty of the proposed algorithm lies in outlier detection while effectively avoiding the dependence on parameters, thus eliminating the influence of outliers on clustering. This article uses real datasets: lypmphography and glass for simulation purposes. The simulation results show that the algorithm proposed in this paper can effectively detect outliers and has a good clustering effect. Furthermore, the experimentation reveals that the outlier detection‐based SCA‐SNN algorithm has the best practical effect on the dataset without outliers, clearly validating the clustering performance of the outlier detection‐based SCA‐SNN algorithm. Furthermore, compared to the other state‐of‐the‐art anomaly detection method, it was revealed that the anomaly detection technology based on outlier mining does not require a training process. Thus, they overcome the current anomaly detection problems caused due to incomplete normal patterns in training samples.

Industry 4.0‐Oriented Chipless RFID Backscatter Signal Variable Polarization Amplitude Deep Learning Coding

Due to the weak network security protection capabilities of control system network protocols under Industry 4.0, the research on industrial control network intrusion detection is still in its infancy. This article discussed and researched the intrusion prevention technology of industrial control networks based on deep learning. According to the electromagnetic scattering theory, the backscatter signal model of the chipless tag was established as a chipless tag structure. Polarized deep learning coding was used for the label; that was, deep learning coding was performed on the copolarization component and the cross‐polarization component at the same time, and a 16‐bit deep learning coding bit number was obtained. The wave crest deep learning coding was used for the split ellipse ring patch label, and the 6‐bit deep learning coding bit number was obtained. Then, the poles of the scattered signal of the tag were extracted to identify the tag. The variable polarization effect was achieved by adopting the dipole resonant unit with the two ends bent. Aiming at the problem of low detection rate caused by the shallow selection of feature classification of intrusion prevention systems, an industrial control network intrusion prevention model based on self‐deep learning encoders and extreme learning machines was proposed to extract features from industrial control network data through deep learning. For accurate classification, the theoretical judgment was also verified through simulation experiments, and it was proved that the detection rate of the model has also improved. It forms a set of industrial control network intrusion prevention system with complete functions and superior performance with data acquisition module, system log module, defense response module, central control module, etc. The matrix beam algorithm was used to extract the poles and residues for the late response, and the extracted poles and residues were used to reconstruct the signal. The reconstructed signal was compared with the scattered signal to verify the correctness of the pole extraction. Finally, the tags were processed and tested in the actual environment, and the measured results were consistent with the theoretical analysis and simulation results.

Research of Network Behavior Pattern Recognition Based on Power Monitoring System

Based on the network security protection of the power monitoring system, this paper proposes a pattern recognition model for the operation behavior of the operation and maintenance personnel of power monitoring system. Through the technical research of the alarm pattern recognition of the network security management platform, and carries out the operation type data of the network security management platform Clustering analysis uses historical data to intelligently train the clustering model. Through training, the risk level classification of the operation behavior is obtained, and then real-time data is introduced into the model for detection, which can judge the degree of risk of the operation event in real time. With the improvement of the K-means clustering algorithm and the application of big data analysis, it further improves the he intelligence level of the platform and provides technical support for the intelligent protection of network.

Information security assessment of wireless sensor networks based on bayesian attack graphs

Network security issues have become increasingly prominent, and information security risk assessment is an important part of network security protection. Security risk assessment based on methods such as attack trees, attack graphs, neural networks, and fuzzy logic has problems such as difficulty in data collection during the assessment process, excessive reliance on expert experience, failure to consider the actual network environment, or ineffective joint application. The qualitative and quantitative information security fuzzy comprehensive evaluation method uses the theory of fuzzy mathematics to better solve the above problems, so that the evaluation method is scientific, comprehensive and operable. To improve the accuracy of information security risk assessment in wireless sensor networks, we propose a fuzzy comprehensive evaluation method based on Bayesian attack graphs. This considers the impact of environmental factors of the assessed system on security risk and the spread of the effects on the Bayesian network. Therefore, this model can reflect possible situations due to network attacks in the wireless sensor network system. The results show that this quantitative evaluation method is applicable to assessing risk in wireless sensor network systems, and the results are more objective and accurate.

Research on Computer Network Risk Prevention and Control Technology in the Information Age

With the advancement of global informatization, cyber risk has become an important factor hindering the informatization process, and cyber security issues have become the focus of public attention. Computer network security incidents include viruses, Trojan horses, worms, network monitoring, hacker attacks, malware, phishing, botnets, etc. In order to help netizens to prevent network security incidents more effectively, in response to the characteristics of network risk prevention data management, network Research and design of risk prevention database and data warehouse. At present, the development of computer technology is very rapid, but there are still many problems in the security of computer networks. Therefore, in order to avoid these problems hindering the development of the computer, the author analyses and discusses the technology related to computer network security protection in the information age, hoping to solve the computer network security problems and make the computer network more secure.