دانلود مقاله یک رویکرد امنیت شبکه مبنی بر یادگیری ماشین
خلاصه
معرفی
مفاهیم مرتبط
قضاوت امنیت شبکه توسط یادگیری ماشینی
نمونه های عملی امنیت شبکه
نتیجه
منابع
Abstract
Introduction
Related Concepts
The Judgment of Network Security By Machine Learning
Practical Examples of Network Security
Conclusion
References
چکیده
امنیت شبکه محتوای اصلی مدیریت شبکه است، اما در فرآیند مدیریت امنیت شبکه، در برابر نفوذ هکرها و تداخل ارتباطی آسیب‌پذیر است که سطح امنیت شبکه را کاهش می‌دهد و باعث از بین رفتن داده‌های ارتباطی حیاتی و نتایج اشتباه می‌شود. ارزیابی امنیتی بر این اساس، این مقاله یک روش یادگیری ماشینی را برای مطالعه ماشینی داده های ارتباطی شبکه، افزایش داده های ارتباطی شبکه و کوتاه کردن زمان ارتباط شبکه پیشنهاد می کند. سپس داده های ارتباطی شبکه توسط پیام ها تجزیه و تحلیل می شوند. در نهایت، از روش یادگیری ماشین برای قضاوت در مورد امنیت داده های ارتباطی و خروجی نتایج ارزیابی امنیتی نهایی استفاده می شود. نتایج نشان می دهد که روش یادگیری ماشینی می تواند تجزیه و تحلیل امنیت شبکه را به دقت انجام دهد، تداخل هکرها و ارتباطات را کاهش دهد و سطح امنیت بیشتر از 9 تا 5 درصد است که بهتر از روش نظارت بر امنیت آنلاین است. بنابراین، روش یادگیری ماشینی می تواند الزامات امنیت شبکه را برآورده کند و برای مدیریت شبکه مناسب است.
Abstract
Network security is the main content of network management, but in the process of network security management, it is vulnerable to hacker intrusion and communication interference, which reduces the level of network security, and causes the loss of crucial communication data and the wrong results of security assessment. Based on this, this paper proposes a machine learning method to machine study network communication data, enhance network communication data, and shorten network communication time. The network communication data is then analyzed by messages. Finally, the machine learning method is used to judge the security of communication data and output the final security assessment results. The results show that the machine learning method can accurately carry out network security analysis, reduce the interference of hackers and communications, and the security level is greater than 9 to 5%, which is better than the online security monitoring method. Therefore, the machine learning method can meet network security requirements and is suitable for network management.
Introduction
Some scholars believe that network security is a comprehensive analysis process of network management [1], and it is necessary to conduct a comprehensive analysis of network communication data and encrypted data. It is prone to problems such as wrong transmission and incomplete network communication data [2]. Currently, network security often has the problems of low-security level [3], long security judgment time, and redundancy of network communication data of different servers in network management [4]. Therefore, some scholars propose to apply intelligent algorithms to network security judgment, identify interference factors such as hackers and communications, and analyze convex analysis to assess network security [5]. However, the data transmission between the central server and the local server is still not ideal [6], and there is a problem of lowsecurity level. To this end, some scholars have proposed a machine science method through the convex analysis of critical values, identifying the more significant probability and prominent values in the key values [7], and conducting approximate analysis of key network communication data to achieve practical judgment of network security [8]. Therefore, based on the machine method, this paper analyzes the key values in network management and verifies the safety judgment results of the method.
Conclusion
For network security machine learning cannot accurately perform network security. Based on this, this paper proposes a machine learning method to set data for image and approximation analysis and uses an approximation to analyze the feature value of safety judgment. Network communication messages and network communication analysis are carried out through machine learning to reduce the mapping of hackers and industries in security judgment. The characteristic value is used as the node for safety judgment analysis to realize the safety judgment of critical values. The results show that the completeness and safety level of the machine learning method is greater than 90%, and there is no significant difference in the changes of image, text and audio, but the difference of the online safety monitoring method is more significant. In the machine method, the time of network security is relatively short, and it is not affected by the prediction level of images, proximity analysis and industry, and security judgment. In contrast, in the machine learning method. The safety judgment time is relatively long, and the time variation range is 6.45~11.57. Therefore, the machine learning method can meet network security requirements and is better than the machine learning method.