Ashen God-Chapter 1042: Delayed Update
As mentioned in the topic, I will update it later tonight. I'm sure you are already familiar, just refresh this chapter by then.
As for the specific time, probably around one or two in the morning, anyway, it's roughly around this time every time.
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Abstract: The rapid development of network information has brought great convenience to residents' lives and production, but it has also brought about many computer network security risks. Therefore, it is imperative to analyze and formulate computer network security management strategies. Based on this, this paper analyzes the reasons for the occurrence of computer network security, and proposes the currently most widely used machine learning security management technologies for it. First, we analyze the design principles of machine learning, the overall architecture, and the network security structure, and then, we introduce in detail the SVM algorithm, BP neural network algorithm, and web technologies, discussing the advantages of machine learning in terms of intelligence and precision in computer network security management predictive analysis techniques. Finally, by describing the implementation of network security management technology and future expectations, a prospect is made. It hopes to provide a more scientific basis for the intelligent, efficient, and accurate implementation of computer network security management based on the advantageous features of 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, the role of internet application technology in technology, life, production, and other fields has become increasingly important [1]. Issues related to network security management are also gradually emerging, such as in 2019, when the Computer Information Security Prevention Center discovered about 11,000 security vulnerabilities on different platforms, primarily consisting of distributed denial-of-service attacks and high-traffic attacks, not only causing difficulties in computer security management but also posing huge security risks to user information protection [2-3]. On this basis, this paper conducts high-quality, intelligent machine learning security management technology to improve computer network traffic safety, information safety, and network platform safety [4]. Machine learning not only orderly unifies the knowledge information in this domain but also plays a crucial role in domain management and deployment. Currently, machine learning technology has been successfully applied in daily shopping, reading, traveling, work, and other fields such as recording user search information and search history in the life domain and storing it in a database for convenient operation [5]; in the work domain, machine learning filters harmful files, advertisements, emails, etc., inside the computer. As machine learning technology continues to develop and innovate, its role and influence in computer network security are increasingly being emphasized. Network security administrators achieve information resource sharing and co-construction through a web-like management model of machine learning to quickly identify and eliminate vulnerabilities in the computer network, improving the level and efficiency of security management. This paper aims to optimize the computing network security management technology model, improve the shortcomings of traditional security management methods, and achieve all-around, multi-level security management modes through intelligent, foundational, and web-like machine learning technology. First, design and build the machine learning security management model, then, detail the key technical support vector machine (Support Vector Machines, SVM) method and BP neural network (Back Propagation, BP) method, and finally assess the effect of the security management of machine learning methods, in the hope of providing scientific technological 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's machine learning system is designed and applied according to the following four principles: (1) Scientificity; (2) Intuitiveness; (3) Security Management Stability; (4) Information Extensibility. On one hand, the four principles help users understand the machine learning security management system and enhance management technology, while on the other, they help interpret machine learning methods and core technologies. Among them, scientificity is the SVM algorithm and BP neural network algorithm adopted in this paper to perform prediction and evaluation of the computer network security situation, compared to traditional security management methods, machine learning methods greatly enhance the accuracy of predicted results of security evaluations and improve the efficiency of security management [6]; intuitiveness displays the current computer system's network security forecast status results and also presents the expected assessment status and historical data layout through visualization, helping network security managers to grasp the computer network security status more intuitively and accurately; security management stability not only ensures the stable operation of each computer system module but also enhances the information security sharing and co-construction between different modules; in terms of information extensibility, machine learning, based on the status of the computer system, presets the extensibility of security protection tools in the security design process.
2.2 Overall Structure Design
Figure 1 shows the overall structure design flow of the computer network security management based on the machine learning method. From Figure 1, it can be seen that the network security management system is mainly divided into user, professional technical engineer module, man-machine interaction module, and computer database security management system module. Among them, the man-machine interaction module is the core of the machine learning method design, mainly consisting 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 performs quantitative evaluation of computer network security and then, according to the evaluation results, collects data information and situational values for relevant forecasts; (2) Machine learning inference primarily performs situational assessments on selected data, generates data in the required format, and then obtains the current computer network security situation through SVM or BP neural network algorithm to predict network security; (3) In terms of knowledge acquisition, it mainly collects network data through computer network inflow/outflow traffic variation values, network transmission control protocol, user data protocol (Transmission Control Protocol, TCP), ratio of TCP digital packet bytes, etc., to analyze and forecast situations; (4) The computer database security management system visualizes the evaluation of security situations for user information and collected situational information, enabling inter-module usage and security management functions.
2.3 Network Data Security Structure Design
This paper further interprets and analyzes network data security based on the overall architecture of machine learning computer network security management, to enhance the understanding of machine learning security management technology for users/complete managers. First, the computer network data pretreatment primarily originates from massive database material. After obtaining the database network data material, relevant feature parameter extraction is carried out, followed by constructing machine learning models (SVM model and BP neural network model) through feature parameters and data material sources. After cross-validation and classification of massive database resources, the machine learning model predicts and evaluates the computer network security situation, constructing corresponding security management systems.
3 Analysis of Key Machine Learning Technologies
3.1 SVM Technology Analysis
Currently, in the field of machine learning, due to the superior precision of SVM algorithm in prediction and evaluation, it is widely used in the domain of computer network security management. Its principle is through the selection of database kernel functions and optimization of model parameters; when multiple kernel functions satisfy a certain eigenvalue, the optimal classification plane is used to select the kernel function. Then, by mapping from low-dimensional space to high-dimensional space, the data results are predicted and classified to realize 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(-\\frac{|x-y|^2}{\\sigma^2})\\) (1) Polynomial Function: \\(k(x, y) = [(x.y) + 1]^d\\) (2) The basic operational process of the SVM algorithm for computer network security management evaluation prediction is as follows: (1) Implement the collection, integration, and transformation of the computer network security hidden danger data through the massive database of computers to prepare for model evaluation analysis; (2) Implement the hyperplane separation by inputting relevant network security hidden danger data and analyze and organize the data through the SVM algorithm; (3) In the training of computer network security related data, adjust the algorithm parameters according to the data characteristics to ensure the accuracy of model evaluation and prediction, and realize a reasonable calculation for multiple classification problems based on the characteristics of SVM model binary classifiers to intelligently serve computer network security management.
3.2 BP Neural Network Analysis
BP neural network is an important and critical discipline in machine learning, which is a precision result prediction model integrating information knowledge acquisition, analysis, and prediction. Figure 2 in this document is a schematic result of BP neural network cross-validation. From Figure 2, it can be seen that BP neural network mainly consists of an \\(X_i\\) input layer, \\(a_i\\) hidden layer, and \\(Y_i\\) output layer, where each neural layer is independent, but there is interconnection between the layers, and the interlayer sharing and co-construction is through weight coefficients. The BP neural network mainly completes the entire BP neural network training process through data set training, multiplying the weight coefficients between feature vectors, then transmitting after the transformation of the data format through the activation function, calculating the error value of the output \\(Y_i\\) layer result and actual result, and adjusting the parameters and weight coefficients to realize the prediction and analysis of computer network security. BP neural network outputs results through multiple iterations of computer network security data information. Its main method is to determine and analyze the parameters of each layer's input and output. When the \\(E(a)\\) value exceeds the threshold value, the threshold is corrected, and the parameter determination is repeated until the threshold is satisfied after several iterations, which means the BP determination result is established. The BP algorithm mainly maps the input or output results, and the data is continuously trained in the BP neural network. After multiple iterative training, the resulting data is more precise and effective, thereby learning the output result data, clarifying the corresponding rules between the training sample input and output. The specific process of computer network security BP neural network training is as shown in formulas 3-4: where the BP network output layer node value is: \\(1()\\sum_k \\sigma(V_{jk} b_{k} + \\beta_j)\\) (3) using the sum of squared errors to determine whether the training process is concluded: \\(\\frac{1}{2}\\sum_k ({O_k - y_k})^2\\) (4) where \\(O_k\\) is the expected output; \\(E\\) represents feeding the output layer's error back to the hidden layer and input layer when the expected target is reached.
3.3 Web Technology
In the computer network domain, Web technology is not only the foundation for internet access but also one of the commonly used technical means in developing application network clients and servers. Access methods are mainly divided into HTTP, URL, and other aspects. Among them, on the Web end, multiple computer technologies are involved, such as Python, C++, and script programs for development and application by integrating, analyzing, and predicting computer data information resources to achieve computer network security management. In the Python language, batch operation adjusts data resources; on the one hand, Python language realizes network security management, on the other hand, greatly enhances security work efficiency. The Web end mainly performs analysis, diagnosis, and adjustments of potential security hazards through computer code language. This both eliminates security risks and reduces economic losses. Currently, Web technology is an indispensable technical means in the machine learning process.
4 Implementation of Machine Learning Security System
4.1 Implementation of Data Collection and Prediction Module
In the process of machine learning, this document first obtains network data information, then conducts computer network security status analysis to ensure the accuracy and crucial role of data information and security status analysis. In the computer network security status perception, the main processes include security status extraction, evaluation, and prediction to complete the data collection of computer network information. In the prediction module, the data collection and analysis process is realized through UDP data byte ratio and ICMP data byte ratio, then data sample training, transmission, analysis, and comparative prediction are carried out through SVM model, BP neural network model, etc., to achieve an intelligent, accurate, and efficient computer network security management system.
4.2 Security Evaluation Effect Analysis
Computer network security evaluation mainly demonstrates the results of security management status evaluation and analysis prediction. In this document, SVM algorithm and BP neural network algorithm are respectively used for training computer database sample data. Then, the prediction results are compared with the actual results for effectiveness validation. If the result after validation deviates significantly from the actual results, machine model parameters will be adjusted and optimized again, and validation and comparison will be performed again to achieve a high precision of prediction results, realize the effective formulation of security management strategies, and achieve high-quality, high-standard security effect evaluation analysis, thereby ensuring computer network information security.
5 Conclusion
At present, the attention to machine learning methods in the field of computer network security management is growing. Based on this, this document first introduces the design principles, overall structure, and network construction of machine learning security management. Subsequently, it introduces the key technical support vector machine (SVM) kernel function prediction data results of machine learning methods; BP neural network as a network training process that integrates knowledge acquisition, analysis, and prediction, and Web end technology (Python) for the diagnosis, analysis, and adjustment of computer network data, etc., thereby achieving computer network security management through the intelligent and precise advantages of machine learning methods.
An Analysis on Computer Network Security Management Under Machine Learning
