Today, the writing is a bit slow, so the update will be a little later, but not too much, probably around one in the morning. At that ti, just refresh this chapter and it'll be ready. It's clear that not having a draft really doesn't work.
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Abstract: The rapid developnt of network information has brought great convenience to residents' lives and production, yet it has also led to various computer network security risks. Thus, analyzing and formulating computer network security managent strategies is imperative. On this basis, this paper analyzes the causes of computer network security issues, and proposes the currently most widely used machine learning security managent technology in response. First, it analyzes the design principles, overall frawork, and network security structure of machine learning. Subsequently, it details the SVM algorithm, BP neural network algorithm, and web-side technology, discussing the advantages of intelligence and accuracy in machine learning in the realm of computer network security managent prediction analysis technique level. Finally, it forecasts future expectations through describing the implentation of network security managent technology. It is hoped that through the characteristic advantages of machine learning, it can provide a more scientific basis for the intelligent, efficient, and accurate realization of computer network security managent based on machine learning.
Keywords: Network Security; SVM thod; BP Neural Network thod; Managent; Implentation
1 Introduction
Currently in China, with the continuous developnt of the economy and intelligent computer information, internet application technology is becoming increasingly important in various fields such as technology, life, and production [1]. Issues concerning network security managent are gradually erging, such as: in 2019, the Computer Information Security Prevention Center in our country discovered approximately 11,000 security vulnerabilities across various platforms, primarily distributed denial-of-service attacks and large-scale traffic attacks, which not only make computer security managent difficult but also pose significant security risks to user information protection [2-3]. Based on this, this paper conducts an orderly high-quality, intelligent machine learning security managent technology to improve computer network traffic safety, information safety, and network platform safety, etc. [4]. Machine learning not only can orderly unify knowledge information in this domain, but also plays a key role in domain managent and deploynt. At present, machine learning technology has been successfully applied in fields such as daily shopping, reading, traveling, and working. For example, in the living domain, machine learning records user search information, search history, and stores them in databases for convenient operations [5]; in the work domain, machine learning filters harmful files, advertisents, emails, etc., within computers. With the continuous developnt and innovation of machine learning technology, its role and influence in computer network security is increasingly emphasized, allowing security administrators to implent a networked managent model through machine learning to achieve shared construction and sharing of information resources, which rapidly identifies and eliminates vulnerabilities existing in computer networks, and enhances security managent level and efficiency. This paper aims to optimize the computer network security managent technology model and improve the shortcomings of traditional security managent approaches through intelligent, foundational, and networked machine learning technology to achieve a comprehensive and multi-leveled security managent model. First, designing and constructing a machine learning security managent model, then detailing key technologies such as Support Vector Machines (SVM) and Back Propagation (BP) thods, and finally evaluating the security managent effectiveness of machine learning thods to provide scientific technical support for computer network security managent technology.
2 Overall Design of Machine Learning Security Managent System
2.1 Design Principles
To master computer network security managent technology based on machine learning, this paper designs and applies the machine learning system according to the following four principles: (1) scientific nature; (2) intuitiveness; (3) stability of security managent; (4) expandability of information. On one hand, the four principles help users understand the machine learning security managent system and enhance managent technology. On the other hand, they assist in interpreting machine learning thods and core technologies. Among them, scientific nature is achieved through adopting the SVM algorithm and BP neural network algorithm to evaluate and predict computer network security situations. Compared to traditional security managent thods, machine learning thods significantly improve the accuracy of prediction results in security evaluations and enhance the efficiency of security managent [6]; intuitiveness not only presents current network security prediction situation results of the computer system but also visualizes the arrangent of expected evaluations and historical data, aiding network security managers in accurately understanding the computer network security status; stability in security managent not only ensures the stable operation of various computer module systems but also enhances the information security sharing and collaborative building between different modules; in terms of expandability of information, machine learning predefines the expandability of security protection tools in the process of security design based on the status of the computer system.
2.2 Overall Structure Design
Figure 1 illustrates the overall structure design process of computer network security managent based on machine learning thods. As shown in Figure 1, the network security managent system primarily consists of user, professional technical engineer modules, human-computer interaction modules, and computer database security managent system modules. Among them, the human-computer interaction module is the core of the machine learning thod design, mainly composed of three parts: explanation chanism, machine learning inference, and knowledge acquisition. The functions of each module and important components are as follows: (1) The user system primarily conducts quantitative assessnt of computer network security and then predicts accordingly based on the evaluation results, collected data information, and situation values; (2) Machine learning inference mainly conducts situation assessnts on selected data parts, generating format data needed, and then uses SVM or BP neural network algorithms to acquire current computer network security situations, performing security evaluations and predictions on the network; (3) In terms of knowledge acquisition, network data collection is mainly carried out through computer network inflow/outflow variation values, Transmission Control Protocol, User Datagram Protocol (Transmission Control Protocol, TCP), TCP digital packet byte ratio, etc., to analyze and predict the situation; (4) The computer database security managent system evaluates security status in a visual manner based on user information and collected situation information, realizing inter-module communication and security managent functions.
2.3 Network Data Security Structure Design
Based on the overall structure of machine learning computer network security managent, this paper further interprets and analyzes network data security to enhance user/complete administrator's understanding of machine learning security managent technology. Firstly, computer network data preprocessing mainly derives from vast database materials. After acquiring database network data materials, relevant feature paraters are extracted. Subsequently, machine learning models (SVM models and BP neural network models) are built using feature paraters and data source materials and, after cross-certification and classification of vast database resources, evaluate and predict computer network security situations using the machine learning model and formulate corresponding security managent systems.
3 Analysis of Key Technologies in Machine Learning
3.1 Analysis of SVM Technology
Currently, in the domain of machine learning, the superior precision of SVM algorithm's prediction assessnt has made it widely applicable in the field of computer network security managent. Its principle is to predict classifications by selecting kernel functions and optimizing model paraters, mapping data from low-dinsional space to high-dinsional space, which achieves network security managent processes. Commonly used kernel functions in SVM algorithms include the following: Radial Basis Kernel Function: k(x, y)=exp(−|x−y|²/σ²) (1) Polynomial Function: k(x, y)=[(x.y) 1]ᵈ (2) The basic operational process of SVM algorithm for evaluation and prediction in computer network security managent is as follows: (1) Collection, integration, and machine transformation process of computer network security hazard data is achieved through massive computer databases in preparation for model evaluation and analysis; (2) By inputting relevant network security hazard data, achieving the separation hyperplane, and analyzing and organizing data through the SVM algorithm; (3) When training computer network security related data, algorithm paraters are adjusted according to data characteristics to ensure accurate model evaluation and prediction. Also, based on the characteristics of SVM model's binary classifiers, a rational calculation for various classification problems is realized, serving computer network security managent intelligently.
3.2 BP Neural Network Analysis
BP Neural Network is an important and critical subject in machine learning, being a model that integrates information knowledge acquisition, analysis, and prediction into precise result prediction. Figure 2 in this paper shows the cross-validation indicative result of BP neural network, from which it is known that BP neural network mainly consists of the Xi input layer, ai hidden layer, and Yi output layer. Each neural layer is independent yet interrelated with others and shares coefficients across layers. BP neural network mainly conducts data set training and multiplies weight coefficients between feature vectors, following which, after transformation through an activation function, data is transmitted. The error value is calculated between the result of the Yi output layer and the actual result, adjusting paraters and weight coefficients to finally complete the entire BP neural network training process, realizing the prediction and analysis of computer network security. BP neural network iterated output results for computer network security data. it primarily determines and analyzes paraters between layers' input and output; when E(a) value exceeds the threshold, the threshold is corrected, and after multiple iterations, eting the threshold ans the BP determination result holds. BP algorithm primarily maps input or output results, data undergoes continuous training within BP neural network, and repeated iteration results in more precise and effective data results, thereby learning from output result data, specifying the correlation rule between input and output of training samples. The specific process of computer network security BP neural network training is shown in formulas 3-4: where the output layer node value of BP network is: 1()kkjjkpjyσVbβ==∑ (3) Training process is judged to end by using error square sum: 211()2kkqkEOy==∑− (4) where: kO is the expected output; E represents transmitting output layer error back to hidden and input layers when reaching a desired target.
3.3 Web Technology
In the computer network field, Web technology is not only the basis for Internet access but also one of the common technical ans in developing network client and server applications. Its access thods mainly include , URL, etc. On the Web side, it involves various computer technologies, such as those related to Python, C , and script programs for developnt and implentation, by integrating, analyzing, and predicting computer data resources to realize computer network security managent. In Python language, data resources are adjusted via batch operations, on one hand realizing network security managent through Python language, on the other significantly boosting security work efficiency. Web side chiefly uses computer code language to analyze, diagnose, and adjust potential security risks. Thus, it eliminates security threats and reduces economic losses. Currently, Web technology is one of the indispensable technical ans in machine learning processes.
4 Implentation of Machine Learning Security System
4.1 Implentation of Data Collection and Prediction Module
In this paper, during the machine learning process, network data information is first obtained, followed by analysis of computer network security status to ensure the accuracy and critical role of data information and security status analysis. In the perception of computer network security status, the main processes include security status extraction, assessnt, and prediction to complete data collection of computer network information. In the prediction module, UDP data byte weight and ICMP data byte weight are used to perform the data collection analysis process, then intelligent, accurate, and efficient computer network security managent system is realized through data sample training, transmission, analysis, and comparative prediction by SVM model, BP neural network model, etc.
4.2 Analysis of Security Assessnt Effectiveness
Computer network security assessnt primarily demonstrates the results of security managent status assessnt and analysis prediction. This paper trains computer database sample data separately with the SVM algorithm, BP neural network algorithm, then effectively verifies prediction results against actual results. If the result significantly differs from the actual result after verification, machine model paraters are adjusted, optimized, etc., and re-verified and compared to achieve high prediction result accuracy, allowing for effective developnt of security managent strategies, resulting in high-quality, high-standard security effect assessnt analysis, thus ensuring computer network information security.
5 Conclusion
Today, the attention to machine learning thods in the field of computer network security managent is increasing. Based on this, this paper first introduces machine learning security managent design principles, overall structure, and network setup, then introduces key technology support thods of SVM kernel function for predicting data results; BP neural network, which integrates knowledge acquisition, analysis, and prediction in a network training process, and Web end technology (Python) for diagnosing, analyzing, and adjusting computer network data, etc. Thus, through the intelligence and precision advantages of machine learning thods, computer network security managent is realized.
Computer engineering managent is an activity utilizing computer, information, communication, Internet, and other technologies for systematic managent activities such as data collection, integration, processing, and analysis. It is also a necessary condition for modern enterprise and information construction and high-quality developnt. With the developnt of the tis, the deep integration of computer engineering managent and electronic information technology has significant importance in optimizing computer data processing flow.
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