微型电脑应用2025，Vol.41Issue(2)：292-296,5.
面向网络空间防御的越权漏洞对抗机器学习检测系统
Machine Learning Detection System of Unauthorized Vulnerability Antagonism for Cyberspace Defense
金磊1
作者信息
- 1. 阿克苏教育学院,初等教育系,新疆,阿克苏 843000
- 折叠
摘要
Abstract
To improve the accuracy of detecting unauthorized vulnerabilities in cyberspace,a machine learning detection system for defending unauthorized vulnerabilities in cyberspace is designed.In the data acquisition module of the system perception lay-er,ICMP scanning technology is used to collect network security vulnerability data,and cluster analysis is used to clean the da-ta.The data is sent to the storage layer for storage by the network communication layer.In the unauthorized vulnerabilities de-tection module of the application layer,the adversarial network is generated by scheduling data input condition residuals using the data call module,and the unauthorized vulnerabilities identification results are output after training.The experimental re-sults show that the system can effectively identify and detect unauthorized vulnerabilities in the cyberspace environment,with high detection accuracy.关键词
网络空间防御/越权漏洞/漏洞检测/机器学习/对抗网络/残差单元Key words
cyberspace defense/unauthorized vulnerability/vulnerability detection/machine learning/countermeasures net-work/residual unit分类
信息技术与安全科学引用本文复制引用
金磊..面向网络空间防御的越权漏洞对抗机器学习检测系统[J].微型电脑应用,2025,41(2):292-296,5.
