Lately I've been getting stuck on writing, so today's update will be a bit late, probably around 1 AM. You can refresh this chapter then.

By the way, I've been really wanting to start a new book lately... After all, this one is reaching nearly 3 million words... Although I did plan to write more than 3 million words initially, I didn't actually expect to write this much... Also, the main reason for getting stuck is that it has become increasingly difficult after entering the late stage.

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We need a virtual unit smaller than the current server virtualization to demonstrate the effect of such virtualization. As early as 2013, Docker technology had begun to take shape. Initially, the project did not receive much attention from the leadership. After joining the Linux Foundation, it adhered to open-source principles under Apache 2.0 and gained widespread attention. Some major internet companies have started to adopt Docker technology as part of their own technology suite. For example, Google has supported its use in products like Paas, showing promising development prospects. As development progressed in the late stage, Docker technology also made a series of improvements and set certain limits on storage content to ensure the smooth operation of large programs. Additionally, the one-to-one relationship between programs and Docker "containers" ensures that the respective program can only access its corresponding "container" and cannot access other content, ensuring orderly and regular operation.

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

1 Introduction

Currently in China, with the continuous development of economy and intelligent computer information, the role of internet application technology in technology, life, production, and other aspects is becoming increasingly important [1]. Issues related to network security management have gradually emerged. For example, in 2019, the Computer Information Security Prevention Center in our country discovered about 11,000 security vulnerability loopholes across different platforms, mainly involving distributed denial of service attacks and high traffic attacks. These not only complicate computer security management but also pose significant security risks to user information protection [2-3]. On this basis, this paper systematically conducts high-quality, intelligent machine learning security management technology to improve computer network flow security, information security, and network platform security, among others [4]. Machine learning can systematically unify knowledge information in this domain and play a crucial role in domain management and deployment. Currently, machine learning technology has been successfully applied in everyday shopping, reading, travel, work, and other fields. For instance, in the field of life, machine learning records user search information and history, storing it in databases for convenient operation [5]; in the field of work, machine learning filters harmful files, advertisements, emails, etc., within computers. With the continuous development and innovation of machine learning technology, its role and impact degree in computer network security are receiving increasing attention. Network security administrators implement an interconnected management model through machine learning to realize information resource sharing and joint construction, quickly identifying and addressing vulnerabilities in the computer network, thereby improving the level and efficiency of security management. This paper aims to optimize computer network security management technology models, addressing the deficiencies in traditional security management methods. Through intelligent, foundational, and networked machine learning technology, a comprehensive, multi-level security management model is achieved. Firstly, the design and construction of a machine learning security management model are undertaken, then detailed elaboration of key technologies such as the Support Vector Machine (SVM) method and Back Propagation (BP) neural network method is provided. Finally, the security management effects of machine learning methods are evaluated to offer scientific technical support for computer network security management technology.

2 Overall Design of the Machine Learning Security Management System

2.1 Design Principles

To grasp computer network security management technology based on machine learning, this paper's machine learning system is designed and applied following four principles: (1) Scientific Nature; (2) Intuitiveness; (3) Security Management Stability; (4) Information Expandability. On one hand, these four principles help users understand the machine learning security management system, enhancing management technology. On the other hand, they assist in explaining machine learning methods and core technologies. Among them, the scientific nature adopts the SVM algorithm and BP neural network algorithm to predict and assess computer network security situations. Compared to traditional security management methods, machine learning methods greatly enhance the accuracy of security assessment predictive results and improve security management efficiency [6]; the intuitiveness not only showcases the current network security predictive status of the computer system but also visualizes the display of prospective assessments and historic data arrays, aiding network security managers in intuitively and accurately grasping the computer network security status; the security management stability not only ensures the stable operation of various module systems within the computer but also enhances the information security sharing and joint construction among different modules; in terms of information expandability, machine learning can preset the expansibility of security tools during the security design process based on the current status of the computer system.

2.2 Overall Structure Design

Figure 1 depicts the overall structural design flow of the computer network security management based on machine learning methods. As shown in Figure 1, the network security management system is mainly divided into modules for users, professional technical engineers, human-machine interaction modules, and computer database security management system modules. Among them, the human-machine interaction module is the core of the machine learning method design, mainly comprising three parts: explanation mechanism, machine learning inference, and knowledge acquisition. The functionalities of each module and main component are as follows: (1) The user system primarily quantifies and evaluates computer network security, subsequently performing relevant predictions based on the evaluation results, collected data information, and status values; (2) The machine learning inference mainly conducts status assessments of selected data, generating required format data, and then uses the SVM or BP neural network algorithm to acquire the current computer network security situation and predict network security; (3) In terms of knowledge acquisition, it mainly conducts network data acquisition, analyzing and predicting statuses through computer network inflow/outflow flow change value, Transmission Control Protocol (TCP), TCP packet byte proportion, etc.; (4) The computer database security management system visualizes and evaluates the security situation based on user information and collected status information, achieving module interoperability and security management functionalities.

2.3 Network Data Security Structure Design

This paper, based on the overall structure of computer network security management in machine learning, further interprets and analyzes network data security to enhance the awareness of users/security administrators on machine learning security management technology. Firstly, the preprocessing of computer network data is primarily sourced from massive database materials. After acquiring database network data, relevant feature parameter extraction is conducted. Subsequently, a machine learning model (SVM model and BP neural network model) is constructed through feature parameters and data sources. Following cross-verification and classification of massive database resources, a machine learning model is used to predict and assess computer network security status, establishing a corresponding security management system.

3 Key Technology Analysis of Machine Learning

3.1 SVM Technology Analysis

Currently, in the machine learning domain, due to the superior precision of the SVM algorithm in prediction and assessment, it is widely applied in the field of computer network security management. The principle involves the selection of database kernel functions and the optimization of model parameters. When multiple kernel functions satisfy a specific eigenvalue, the optimal classification plane is used for kernel function selection. Subsequently, mapping from a low-dimensional space to a high-dimensional space is performed to predict and classify the data results, thus achieving the network security management process. Currently, the commonly used kernel functions in the SVM algorithm are as follows: 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 the SVM algorithm for computer network security management assessment and prediction is as follows: (1) Achieving the collection, integration, and machine transformation process of computer network security risk information data via massive computer databases to prepare for model assessment analysis; (2) Implementing hyperplane separation through inputting related network security risk information and data analysis arrangement via the SVM algorithm; (3) During computer network security-related data training, adjusting algorithm parameters according to data features to ensure accurate model assessment and prediction, while rationally calculating multiple classification problems through the characteristics of SVM model binary classifier, intelligently serving computer network security management.

3.2 BP Neural Network Analysis

BP neural network is an important and critical discipline in machine learning, being a precise result prediction model integrating information knowledge acquisition, analysis, and prediction. Figure 2 in this paper shows the cross-validation demonstration result of BP neural network. From Figure 2, it is evident that the BP neural network mainly consists of the Xi input layer, ai hidden layer, and Yi output layer. Each neural layer is both independent and interconnected, with layers sharing and co-constructing through weight coefficients. The BP neural network primarily trains the dataset, and multiplies weight coefficients between feature vectors. Subsequently, data format conversion through excitation functions and transmission is performed. The discrepancy between the Yi output layer's result and the actual result is calculated to adjust parameters and weight coefficients, finally completing the entire BP neural network training process, achieving prediction analysis of computer network security. The output result of BP neural network for computer network security data information after multiple iterations is primarily through the determination and analysis of parameters for each layer's input and output. When the E (a) value exceeds a threshold, the threshold is corrected. After multiple iterations and meeting the threshold, the BP judgment result is established. The BP algorithm primarily maps input or output results. Data undergoes continuous training in the BP neural network, and after multiple iterative training sessions, more precise and effective data results are obtained, further learning output result data and identifying corresponding rules between training sample inputs and outputs. The computer network security BP neural network training process is specifically shown in formulas 3-4: The BP network output layer node value is: 1 () ∑ ∑ j j k k k j y V b β σ = + (3) The square sum of error is used to determine whether the training process is ended: 2()211 k k q k E O y == − ∑ (4) In the formula: k O is the desired output; E is when the expected goal is achieved, the error of the output layer is back-propagated to the hidden layer and input layer.

3.3 Web Technology in the Field of Computer Networks

In the domain of computer networks, Web technology is both the foundation for internet access and one of the commonly used technical means in the development of network client and server applications. Its access methods are mainly divided into HTTP, URL, etc. Among them, on the Web end, various computer technologies are involved, such as Python, C++, and scripting programs in developing applications, accomplishing computer network security management through integration, analysis, and prediction of computer data information resources. In Python language, by running and adjusting data resources in batches, on one hand, network security management is realized through Python language, and on the other hand, security work efficiency is greatly improved. The Web end primarily uses computer code language to analyze, diagnose, and adjust potential security risks. In doing so, it eliminates security risks and reduces economic losses. Currently, Web end technology is an indispensable technical means in the machine learning process.

4 Implementation of Machine Learning Security System

4.1 Implementation of Data Acquisition and Prediction Module

In the machine learning process, this paper first acquires network data information and then conducts computer network security status analysis to ensure the accuracy and key role of data information and security status analysis. In computer network security status perception, the primary procedures involve status extraction, evaluation, and prediction to complete the acquisition of computer network information data. In the prediction module, the data acquisition and analysis process is realized through UDP data byte weight and ICMP data byte weight, and then through training, transmission, analysis, and comparison prediction of sample data using SVM models, BP neural network models, etc., an intelligent, accurate, and efficient computer network security management system is realized.

4.2 Security Assessment Effect Analysis

The computer network security assessment primarily displays the results of the security management status assessment and analysis prediction. This paper uses the SVM algorithm and BP neural network algorithm to train computer database sample data separately. Subsequently, the prediction results and actual results are validated for effectiveness. If the difference between the verified results and actual results is large, machine model parameter adjustment and optimization are performed again, followed by validation and comparison. This ensures high precision in prediction results, achieves effective formulation of security management strategies, and attains high-quality, high-standard security effect assessment analysis, thereby safeguarding computer network information security.

5 Conclusion

Currently, the attention to machine learning methods in the field of computer network security management is increasing. Based on this, this paper first introduces the machine learning security management design principles, overall structure, and network construction, and then introduces the key technologies of machine learning methodology, such as support vector machine (SVM) for kernel function prediction results; BP neural network integrates knowledge acquisition, analysis, and prediction processes for network training, and Web end technology (Python) for the diagnosis, analysis, and adjustment of computer network data, and so on. By leveraging the intelligence and precision advantages of machine learning methods, computer network security management is achieved.

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