基于强化学习的多机系统稳定性控制策略研究
摘 要
网络攻击给各类信息系统带来了严重的安全隐患，网络攻击检测与防御是信息安全领域的重要研究方向。本文基于人工智能技术，研究并实现基于深度学习的网络攻击检测和防御系统。本研究将深度学习应用于网络流量分析和多维度信息特征提取，从而提高了网络攻击检测的准确率和效率。此外，结合决策树算法和流量过滤技术，实现了对网络攻击的有效防御，提高了网络安全性。最后通过实验测试验证了所提出系统的可行性和有效性。
关键词：人工智能；网络安全；深度学习；网络攻击；决策树
Research on Network Attack Detection and Defense Based on Artificial Intelligence
【Abstract】Network attacks have brought serious security risks to various information systems, and network attack detection and defense are important research directions in the field of information security. This article is based on artificial intelligence technology to research and implement a network attack detection and defense system based on deep learning. This study applies deep learning to network traffic analysis and multi-dimensional information feature extraction, thereby improving the accuracy and efficiency of network attack detection. In addition, the combination of decision tree algorithm and traffic filtering technology has achieved effective defense against network attacks and improved network security. Finally, the feasibility and effectiveness of the proposed system were verified through experimental testing.
【Keywords】artificial intelligence; Network security; Deep learning; Network attacks; Decision Tree
目 录
前言 1
一、相关技术介绍 2
（一）网络攻击分类和特点 2
（二）人工智能在网络攻击检测中的应用 2
（三）深度学习技术介绍 2
（四）决策树算法和流量过滤技术介绍 3
二、系统设计与实现 4
（一）问题定义和策略设计 4
（二）系统架构设计 4
（三）数据获取和特征提取 5
（四）基于深度学习的攻击检测模型实现 5
（五）基于决策树的攻击防御模型实现 6
三、实验与分析 8
（一）实验平台和数据集 8
（二）实验结果 8
（三）实验分析和比较 9
结语 10
参考文献 11
