基于深度学习算法的OpenStack网络攻防实训系统优化研究  

Optimization of OpenStack Network Attack and Defense Training System Based on Deep Learning Algorithm

作　　者：郁杰[1] YU Jie(Zhangjiakou Vocational and Technical College,Zhangjiakou Hebei 075000,China)

机构地区：[1]张家口职业技术学院,河北张家口075000

出　　处：《信息与电脑》2023年第21期87-89,共3页Information & Computer

基　　金：中国高校产学研创新基金——一代信息技术创新项目“基于私有云平台的网络攻防实训系统开发”(项目编号:2021ITA09006)。

摘　　要：在日益复杂的网络安全威胁下,文章的主要目标是通过提升深度学习模型的性能和改进实验环境与用户界面,以提高OpenStack网络攻防实训系统的操作效率,改善用户体验。文章结合最新的深度学习算法和用户友好的界面设计,为培养网络安全专业人才提供更高质量的实际训练平台。In the current increasingly complex network security threats,the main goal of this study is to significantly improve the operational efficiency and user experience of the OpenStack network attack and defense training system by improving the performance of deep learning models and improving the experimental environment and user interface.By combining the latest deep learning algorithms and user-friendly interface design,we provide a higher quality practical training platform for cultivating network security professionals.

关 键 词：深度学习模型性能 OpenStack网络攻防 用户体验 

分 类 号：TP309.2[自动化与计算机技术—计算机系统结构]