Network Security Technology Research Articles

Analysis of Computer Network Security and Prevention Technology

Analysis of Computer Network Security and Prevention Technology

Construction of Network Culture Security Indicator System Based on Deep Learning Algorithm

The Internet has become a key information infrastructure in China. However, in recent years, China's network security incidents have been frequent, thus posing a great threat to relevant software, hardware and other information infrastructure. It is very important to establish a large-scale network security situation analysis system based on existing network security infrastructure and technology to improve the processing capabilities of controlling, analyzing, and predicting network security situations. Network security situation assessment is an emerging research hotspot that has emerged in recent years. This article first defined a series of concepts such as the security indicator ring system, network culture, and deep learning algorithms, and then conducted comparative analysis through experiments. Compared with the original dataset, the classification accuracy after feature selection increased by 33.6%.

Retracted: Network Security Technology of Supply Chain Management Based on Internet of Things and Big Data

Retracted: Network Security Technology of Supply Chain Management Based on Internet of Things and Big Data

Research on the Application of Network Security Technologies in the Network Security Operations and Maintenance Process

Research on the Application of Network Security Technologies in the Network Security Operations and Maintenance Process

Design and Research of Teaching and Training Platform Based on Industrial Internet and Intelligent Manufacturing

Industrial internet and intelligent manufacturing application training platform is a comprehensive teaching and training platform for industrial internet, which contains a complete industrial internet architecture, including: sensing equipment, actuator, wireless sensor network, wired sensor network, industrial master control organization, industrial Internet cloud platform, industrial big data analysis platform. The industrial internet application technology training platform uses emerging technologies such as industrial internet, embedded, big data, mobile application, artificial intelligence, intelligent manufacturing, and built-in industrial internet cloud platform to provide teaching, practical training and scientific research support for the upgrading and reform of relevant disciplines in vocational colleges. The industrial internet and intelligent manufacturing training platform provides hardware and software resources with three layers of architecture, including equipment layer, platform layer and application layer, providing supporting teaching and practical training resources for related teaching. Industrial internet application technology training platform can meet the needs of industrial Internet teaching and training courses, including: "Industrial Ethernet and Network Equipment", "Network Security Technology", "Industrial Internet of Things", "Industrial Automation Control", "Network System Software Design", "Machine Vision Technology", "Manufacturing Execution System (MES) Application Technology" and so on.

Optimization Strategy of Computer Network Security Technology in Big Data Environment

With the rapid development of computer technology and big data technology, the security of computer network has become a key problem that restricts its development. The improved new technology also causes huge network security problems. The continuous development of big data will improve the design level of computer network security system. However, as a media, the computer's requirements for computer network security will also increase. Therefore, it is necessary to continuously strengthen the security of computer network system, improve users' usage and improve the network security status. It is a very effective means to improve the network security through legal restrictions, so it has become the first task. With the continuous improvement of information and data transmission speed, information and data will accumulate in the process of information technology utilization, thus causing security problems. The computer network security technology in the big data environment seriously affects the development of information technology and the development of the whole society.

Speech denoising algorithm and artificial intelligence application in network security

Speech denoising refers to people finding useful information in noise-affected speech data and processing it. This technology is an important part of speech signal processing technology. With the continuous progress of science and technology, its application in various fields is gradually improving, among which voice noise reduction technology has also been well developed. Especially in recent years, with the rapid development of artificial intelligence science and technology, anti-network speech denoising technology based on deep learning has been derived. Compared with traditional speech denoising techniques, it has better performance in resolving speech data distortion and denoising results. However, in practical applications, this method still has some shortcomings, such as: some unique noises are not removed, and the effective information extraction is distorted under the background of high-intensity noise. In response to these outstanding problems, this research is based on the anti-network speech denoising technology, combined with the Wiener filter speech denoising algorithm, and applies artificial intelligence technology to network security technology management. In this paper, two algorithms, COVL and SSNR, are used to compare the original input speech data signal with noise and the speech data signal preprocessed by Wiener filtering. The additional information is input into the generation network and the discriminant network, and the additional information is used to guide the data generation. The least squares loss function of the binary coding algorithm is used to replace the cross entropy loss function, so that the original discriminant network loss function is not changed, which improves the accuracy of discrimination. This improved technology is superior in terms of speed, productivity and improved accuracy. While building the system, mature network technologies and equipment applications are fully applied, and speech de-noising technology is used for network security situation assessment and prediction simulation tests. The simulation test results show that the network security situation assessment is reasonable and effective, and the prediction is accurate in real time.

Brief Discussion on the Application of Computer Information Technology in Network Security under the Background of Big Data

Brief Discussion on the Application of Computer Information Technology in Network Security under the Background of Big Data

Application of Computer Network Security Technology in Network Maintenance

Application of Computer Network Security Technology in Network Maintenance

An Analysis of Internet of Things Computer Network Security and Remote Control Technology

In order to solve the problem of the specific application of Internet of Things computer network security monitoring technology in infrastructure construction, a method of remote network monitoring system design is proposed, taking tunnel security construction as an example. According to the characteristics of construction tunnel safety monitoring, this method provides theoretical guidance from several aspects, such as computer remote monitoring, data decision analysis, safety control, personnel positioning, and data management. Through experiments, it is found that in the safety detection experiment, when the data is extracted within 2 minutes for detection, the detection accuracy is higher than 80%, and if it is 4 minutes, the detection accuracy is higher than 90%; in the response time test, the local control response time is generally less than 1 s. In the remote control response time test, the response time can generally be within 3 s, and it may be greater than 5 s. The designed construction tunnel monitoring system transmits the collected data to the monitoring terminal remotely. Then, the sensor data is analyzed for timing decision-making. This method can effectively calculate the safety status of each monitoring unit of the current tunnel according to a certain algorithm and formulate corresponding emergency measures according to the safety status.

A Research on the Application of Virtual Network Technology in Computer Network Security

In the computer field, network security is a crucial integrant. It is necessary to pay attention on the application of virtual network technology, so as to raise the standard of computer network security to a new level [2]. In view of this, this paper will analyze the application of virtual network technology in computer network security and propose some strategies for future reference.

The Application of Computer Network Security Technology in Network Security Maintenance

The Application of Computer Network Security Technology in Network Security Maintenance

The Technical Research on the Assessment of Network Security Situation Based on D-S Evidence Theory

Abstract With the increasing importance of network security, more attention is paid to the research on network security situation awareness, while network security situation assessment is the core of situation awareness. The existing situation assessment technology has many problems, such as unitary methodology, large-scale subjective factors and so on, affecting the accuracy of situation assessment. Combined with the traditional network security technology, this paper sets up a three-layer evaluation index system, and puts forward an overall network security situation evaluation model based on hidden Markov model, PageRank algorithm and D-S evidence theory, through which, the overall situation is evaluated after using different fusion algorithms in different fusion stages. The simulation result shows that the above assessment method is more accurate and efficient than the method using single model.

Network Security Technology of Supply Chain Management Based on Internet of Things and Big Data.

In order to improve the network security of supply chain management based on the Internet of Things and big data, this paper studies the Internet of Things and big data technology and analyzes the data processing process of the Internet of Things. On the premise that the terminal device meets the requirements of delay and energy consumption, a data offloading encryption model is established to offload data to multiple edge servers for encryption. In addition, this paper proposes an optimization problem including overall security, delay, and equipment energy consumption and establishes a mathematical model that maximizes overall security under the condition of satisfying the requirements of delay and equipment energy consumption. From the simulation data, it can be seen that the network security technology of supply chain management based on the Internet of Things and big data proposed in this paper can achieve effective management and control of supply chain nodes and improve their information security.

Computer Network Confidential Information Security Based on Big Data Clustering Algorithm

Attacks on network systems are becoming more and more common, the current state of increasingly sophisticated attack methods, the emergence of intrusion prevention technology is the inevitable result of the development of computer technology and network technology, and research on intrusion prevention has become a new focus of network security technology research in recent years. In order to ensure the security of computer network confidential information, the authors propose a semisupervised clustering intrusion detection algorithm. An overview of machine learning, followed by an explanation of the theory of cluster analysis, simulation experiments were carried out using the K -means algorithm and the semisupervised clustering algorithm proposed by the author, for 10,000 records, the K -means clustering algorithm and the semisupervised clustering algorithm described in this paper are used, respectively, and intrusion detection data tests were performed. At the same time, different K values were selected, three datasets were selected from “kddcup.newtestdata_10_percent_corrected,” the test data were tested separately, and their average value was taken as the test result. From the simulation results, the detection rate of the semisupervised clustering algorithm is higher than that of the K -means clustering algorithm, and the false alarm rate and K -means algorithms have also been improved. Therefore, the author’s semisupervised algorithm enhances the stability of the system, and the performance of the K -means algorithm is improved to a certain extent. When the value of K gradually increases, the false alarm rate also increases; however, when K is 20, the detection rate is maximized, from this, it can be known that when K is 20, its detection rate reaches 91.76%, and the false alarm rate is 8.54%. The detection rate of the author’s algorithm is significantly higher than the other two algorithms, the false positive rate is slightly higher than K -means, and the false positive rate is lower than that of the other algorithm, proving the superior performance of our algorithm.

A data plane security model of segmented routing based on SDP trust enhancement architecture

Segment routing (SR) technology is a new network functional technology derived from MPLS technology and based on SDN. Combining SR with software-defined perimeter (SDP), a new network security technology, is expected to solve the traditional problems such as data monitoring, denial of service, and new threats such as loop attack and label detection faced by SR data plane. Focusing on the security management of access devices in the SR data plane, first, this paper proposes an SR security model SbSR (SDP-based SR) based on SDP trust enhancement architecture, then, two-level SDP AH trust verification mechanism and 4 trust management mechanisms including initial trust value, trust evaluation, trust renewal, trust inheritance are designed. In the trust evaluation mechanism as the core of the model, System cloud grey model (1,1) weighted Markov prediction model is introduced to obtain real-time trust based on the historical behavior of device nodes, and 4 indexes, namely benign message ratio, loyal forwarding ratio, forwarding ratio stationarity coefficient, packet rate stationarity coefficient, are introduced to distinguish malicious devices from normal devices. Finally, the simulation test results of 5 security functions and security costs show that the proposed architecture can solve port scanning, traffic monitoring, topology detection, loop attack, and DoS attack of SR network data plane with an average access delay cost of 2.84 s for each new network agent, and realize multi-faceted protection of SR network data plane.

Computer Deep Learning Network Security Vulnerability Detection Based on Virtual Reality Technology

In order to detect the computer network security technology vulnerabilities due to various factors, the normal operation of the computer network must be ensured, the user’s confidential information must be protected, and it is proposed that the analysis and research on security vulnerability detection must be strengthened. This study introduces the working principle of the network security monitoring system, analyzes the key technologies involved in the system development process and network programming technology, gives the overall architecture of the system, and designs the processing flow of the monitoring function. The test and analysis of the system show that the design of the system has achieved the expected design goal. The design of the system has achieved the expected design goal. The five test points can meet the standard time specified in the demand analysis process. The time difference of all module test points in the test is less than 3 s. The system can realize the remote acquisition and real-time monitoring of the network access, file system operation, system operation status, and other information of the controlled computer. Through the test and analysis of the system, it is shown that the system has achieved the expected design goal, the working state problem, and can meet the functional requirements of internal network security monitoring. It can be applied to enterprises, institutions, and departments that have higher requirements for intranet information security.

Visualization and Analysis Model of Industrial Economy Status and Development Based on Knowledge Graph and Deep Neural Network.

This paper adopts knowledge mapping combined with a deep neural network algorithm to conduct in-depth research and analysis on the current situation and development of the industrial economy and designs a visual analysis model of economic development based on knowledge mapping combined with a deep neural network algorithm. Cultivate the concept of coordinated development and legal system of the subject, improve the awareness of network security and integrity self-discipline of the subject, improve the level of network hardware equipment manufacturing, improve the level of network platform construction, build a network security technology prevention system, improve the repair system of network information alienation, set up a specialized agency setting for the coordinated development of network ecology and industrial economy, and increase the capital investment in network infrastructure and network information technology research and development. A framework of breadth and depth recommendation ranking based on a knowledge graph is proposed and implemented. This paper provides a visual analysis method to sort and classify multivariate data. The method first determines users' preferences through their interactive operations, calculates the weights of each attribute according to the users' preference model, then uses the obtained attribute weight sets to sort the whole data set, and finally completes the category classification according to the sorting results and the users' markings on some data. The visual display allows users to intuitively perform data sorting and classification operations and quickly understand the characteristics and category features of the data. The framework achieves modeling and integration of knowledge graph neighborhood information from breadth dimension and depth dimension to realize personalized recommendation sorting and improves the F1 metrics by 8.59%, 14.36%, and 15.22% on the public datasets Amazon-book, Yelp2018, and ILast-FM compared with the previous optimal model.

Research progress on wireless sensor network (WSN) security technology

Abstract Wireless sensor network (WSN) has been a frontier technology field that integrates a variety of technologies and has a wide range of applications in the military. Network security is of great significance in military warfare. First, this paper outlines the technical advantages of the application of military based on the technical characteristics of WSN. In military applications, WSN has been used in modern military combat while WSN features easy and fast deployment, high concealment, good fault tolerance, high accuracy and other obvious advantages. Moreover, from the perspective of network security, this paper reviews the research progress of network security technology at the network level and data level. The network level includes security protocols, key management, and secure routing technology, and key management includes pre-shared key mode and random key pre-distribution mode. At the data level, error control and fault-tolerant algorithms are mainly included. Automatic retransmission and forward error correction techniques are traditional error control methods, which should be combined with the specific situation of WSN to propose the corresponding fault-tolerant mechanism. Finally, this paper summarizes the practical applications of WSN in the military. From battlefield reconnaissance and target localization monitoring to destruction effect control and anti-terrorism equipment research, network security has been a top priority. Against the background of practical military applications, this paper summarizes the research significance of network security technology.

Data-Driven Zero Trust Key Algorithm

In today’s information society, network security is a crucial issue. Network security technology is changing as a result of the development of emerging technologies such as big data, cloud computing, and artificial intelligence. Data-driven (DD) security has emerged as a new network security technology development direction. The key technologies for DD network security are discussed in depth in this paper. A data security protection system is designed from the perspective of ZT, based on advanced security concepts and technologies developed by ZT, as well as a foreign data security governance framework. The number of alarms generated per hour is counted, removal rules are defined, and real-time rule matching is performed to eliminate false alarms based on different combinations of attributes. By analyzing the security data generation rules and internal relations, a security aggregation method can reduce redundant data and improve alarm quality.