摘 要
随着网络技术的迅猛发展，局域网环境下面临着日益严峻的恶意软件传播威胁，传统安全防护手段难以有效应对复杂多变的攻击模式。为此，本研究旨在构建基于深度包检测的局域网恶意软件传播监控系统，以实现对恶意软件传播行为的精准识别与实时监测。研究采用深度包检测技术，结合机器学习算法，深入分析网络流量特征，提取能够表征恶意软件传播的关键属性，建立高效的特征库。通过构建模拟局域网环境进行实验测试，结果表明该系统能够准确区分正常流量与恶意流量，对多种类型恶意软件传播具有较高的识别率，且误报率较低。
关键词：深度包检测 恶意软件传播 局域网安全
Abstract
With the rapid development of network technology, the LAN environment is faced with the increasingly severe threat of malicious software communication, and the traditional security protection means are difficult to effectively deal with the complex and changeable attack modes. To this end, this study aims to build a LAN malware propagation monitoring system based on deep packet detection to realize accurate identification and real-time monitoring of malware propagation behavior. The research adopts deep package detection technology and combines with machine learning algorithm to deeply analyze the characteristics of network traffic, extract the key attributes that can represent the propagation of malware, and establish an efficient feature library. By building a simulated LAN environment, the results show that the system can accurately distinguish normal traffic from malicious traffic, and has a high recognition rate of various types of malicious software propagation, and a low false alarm rate.
Keyword:Deep Packet Inspection Malware Propagation Local Area Network Security
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3本文研究方法 2
2深度包检测技术分析 2
2.1深度包检测原理 2
2.2关键技术解析 3
2.3技术挑战与应对 3
3局域网恶意软件传播特征 4
3.1传播途径识别 4
3.2行为模式分析 5
3.3特征提取方法 5
4监控系统设计与实现 6
4.1系统架构设计 6
4.2核心功能模块 6
4.3性能优化策略 7
结论 8
参考文献 9
致谢 10
随着网络技术的迅猛发展，局域网环境下面临着日益严峻的恶意软件传播威胁，传统安全防护手段难以有效应对复杂多变的攻击模式。为此，本研究旨在构建基于深度包检测的局域网恶意软件传播监控系统，以实现对恶意软件传播行为的精准识别与实时监测。研究采用深度包检测技术，结合机器学习算法，深入分析网络流量特征，提取能够表征恶意软件传播的关键属性，建立高效的特征库。通过构建模拟局域网环境进行实验测试，结果表明该系统能够准确区分正常流量与恶意流量，对多种类型恶意软件传播具有较高的识别率，且误报率较低。
关键词：深度包检测 恶意软件传播 局域网安全
Abstract
With the rapid development of network technology, the LAN environment is faced with the increasingly severe threat of malicious software communication, and the traditional security protection means are difficult to effectively deal with the complex and changeable attack modes. To this end, this study aims to build a LAN malware propagation monitoring system based on deep packet detection to realize accurate identification and real-time monitoring of malware propagation behavior. The research adopts deep package detection technology and combines with machine learning algorithm to deeply analyze the characteristics of network traffic, extract the key attributes that can represent the propagation of malware, and establish an efficient feature library. By building a simulated LAN environment, the results show that the system can accurately distinguish normal traffic from malicious traffic, and has a high recognition rate of various types of malicious software propagation, and a low false alarm rate.
Keyword:Deep Packet Inspection Malware Propagation Local Area Network Security
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3本文研究方法 2
2深度包检测技术分析 2
2.1深度包检测原理 2
2.2关键技术解析 3
2.3技术挑战与应对 3
3局域网恶意软件传播特征 4
3.1传播途径识别 4
3.2行为模式分析 5
3.3特征提取方法 5
4监控系统设计与实现 6
4.1系统架构设计 6
4.2核心功能模块 6
4.3性能优化策略 7
结论 8
参考文献 9
致谢 10
