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 time, 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 development 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 management 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 management technology in response. First, it analyzes the design principles, overall framework, 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 management prediction analysis technique level. Finally, it forecasts future expectations through describing the implementation of network security management 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 management based on machine learning.

Keywords: Network Security; SVM Method; BP Neural Network Method; Management; Implementation

1 Introduction

Currently in China, with the continuous development 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 management are gradually emerging, 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 management 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 management 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 management and deployment. 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, advertisements, emails, etc., within computers. With the continuous development and innovation of machine learning technology, its role and influence in computer network security is increasingly emphasized, allowing security administrators to implement a networked management 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 management level and efficiency. This paper aims to optimize the computer network security management technology model and improve the shortcomings of traditional security management approaches through intelligent, foundational, and networked machine learning technology to achieve a comprehensive and multi-leveled security management model. First, designing and constructing a machine learning security management model, then detailing key technologies such as Support Vector Machines (SVM) and Back Propagation (BP) methods, and finally evaluating the security management effectiveness of machine learning methods to provide scientific technical support for computer network security management technology.

2 Overall Design of Machine Learning Security Management System

2.1 Design Principles

To master computer network security management 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 management; (4) expandability of information. On one hand, the four principles help users understand the machine learning security management system and enhance management technology. On the other hand, they assist in interpreting machine learning methods 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 management methods, machine learning methods significantly improve the accuracy of prediction results in security evaluations and enhance the efficiency of security management [6]; intuitiveness not only presents current network security prediction situation results of the computer system but also visualizes the arrangement of expected evaluations and historical data, aiding network security managers in accurately understanding the computer network security status; stability in security management 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 management based on machine learning methods. As shown in Figure 1, the network security management system primarily consists of user, professional technical engineer modules, human-computer interaction modules, and computer database security management system modules. Among them, the human-computer interaction module is the core of the machine learning method design, mainly composed of three parts: explanation mechanism, machine learning inference, and knowledge acquisition. The functions of each module and important components are as follows: (1) The user system primarily conducts quantitative assessment 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 assessments 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 management system evaluates security status in a visual manner based on user information and collected situation information, realizing inter-module communication and security management functions.

2.3 Network Data Security Structure Design

Based on the overall structure of machine learning computer network security management, this paper further interprets and analyzes network data security to enhance user/complete administrator's understanding of machine learning security management technology. Firstly, computer network data preprocessing mainly derives from vast database materials. After acquiring database network data materials, relevant feature parameters are extracted. Subsequently, machine learning models (SVM models and BP neural network models) are built using feature parameters 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 management 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 assessment has made it widely applicable in the field of computer network security management. Its principle is to predict classifications by selecting kernel functions and optimizing model parameters, mapping data from low-dimensional space to high-dimensional space, which achieves network security management 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 management 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 parameters 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 management 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 parameters 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 parameters between layers' input and output; when E(a) value exceeds the threshold, the threshold is corrected, and after multiple iterations, meeting the threshold means 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 means in developing network client and server applications. Its access methods mainly include HTTP, URL, etc. On the Web side, it involves various computer technologies, such as those related to Python, C++, and script programs for development and implementation, by integrating, analyzing, and predicting computer data resources to realize computer network security management. In Python language, data resources are adjusted via batch operations, on one hand realizing network security management 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 means in machine learning processes.

4 Implementation of Machine Learning Security System

4.1 Implementation 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, assessment, 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 management 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 Assessment Effectiveness

Computer network security assessment primarily demonstrates the results of security management status assessment 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 parameters are adjusted, optimized, etc., and re-verified and compared to achieve high prediction result accuracy, allowing for effective development of security management strategies, resulting in high-quality, high-standard security effect assessment analysis, thus ensuring computer network information security.

5 Conclusion

Today, the attention to machine learning methods in the field of computer network security management is increasing. Based on this, this paper first introduces machine learning security management design principles, overall structure, and network setup, then introduces key technology support methods 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 methods, computer network security management is realized.

Computer engineering management is an activity utilizing computer, information, communication, Internet, and other technologies for systematic management activities such as data collection, integration, processing, and analysis. It is also a necessary condition for modern enterprise and information construction and high-quality development. With the development of the times, the deep integration of computer engineering management and electronic information technology has significant importance in optimizing computer data processing flow.

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