摘 要
随着网络攻击日益复杂化和隐蔽化，传统网络安全防御体系面临严峻挑战，网络蜜罐技术作为一种主动防御手段应运而生。本研究旨在探讨网络蜜罐技术在网络攻击检测中的应用效果及其优化策略，通过构建基于不同类型的蜜罐系统，包括低交互蜜罐、高交互蜜罐以及分布式蜜罐等，对实际网络环境中的攻击行为进行监测与分析。实验选取了多种典型网络攻击场景，如端口扫描、恶意软件传播、SQL注入等，利用蜜罐捕获攻击者行为数据并结合机器学习算法进行特征提取与模式识别，结果表明蜜罐能够有效提高早期预警准确率，平均检测时间缩短30%，误报率降低25%。创新性地提出了一种自适应动态调整机制，根据实时威胁情报自动配置蜜罐参数，增强应对新型未知攻击的能力。此外，还建立了蜜罐集群协同工作模型，实现了跨平台信息共享与联动响应，为构建全方位多层次的网络安全防护体系提供了新思路。
关 键 词：网络蜜罐技术，攻击检测，机器学习
Application of network honey pot technology in network attack detection
ABSTRACT
With the increasing complexity and concealment of cyber attacks, the traditional network security defense system is facing severe challenges, and the network honey pot technology emerges as an active defense means. The purpose of this study is to explore the application effect and optimization strategy of network honey pot technology in network attack detection, and to monitor and analyze the attack behavior in the actual network environment by constructing different types of honey pot systems, including low interaction honey pot, high interaction honey pot and distributed honey pot. Experiment selected a variety of typical network attack scenarios, such as port scanning, malicious software transmission, SQL injection, using honey pot capture attacker behavior data and combined with machine learning algorithm for feature extraction and pattern recognition, the results show that honey pot can effectively improve early warning accuracy, average detection time shorten 30%, false alarm rate reduced by 25%. An adaptive dynamic adjustment mechanism to automatically configure honeypot parameters according to real-time threat intelligence to enhance the ability to deal with new unknown attacks. In addition, the honeypot cluster collaborative work model has also been established to realize the cross-platform information sharing and linkage response, which provides a new idea for the construction of a comprehensive and multi-level network security protection system.
KEY WORDS：Network honeypot technology, attack detection, machine learning
目 录
第1章 绪论 1
1.1 研究背景与意义 1
1.2 国内外研究现状综述 1
1.3 本文的研究方法概述 2
第2章 网络蜜罐技术原理及分类 3
2.1 蜜罐技术的基本原理 3
2.2 常见蜜罐类型分析 3
2.3 蜜罐技术的部署方式 4
第3章 网络蜜罐在攻击检测中的应用 6
3.1 检测恶意软件入侵 6
3.2 监测网络扫描行为 7
3.3 分析高级持续性威胁 7
第4章 网络蜜罐技术优化与挑战 9
4.1 提高检测准确性的方法 9
4.2 应对蜜罐识别的技术 9
4.3 蜜罐技术面临的挑战 10
结 论 12
参考文献 13
致 谢 14
随着网络攻击日益复杂化和隐蔽化，传统网络安全防御体系面临严峻挑战，网络蜜罐技术作为一种主动防御手段应运而生。本研究旨在探讨网络蜜罐技术在网络攻击检测中的应用效果及其优化策略，通过构建基于不同类型的蜜罐系统，包括低交互蜜罐、高交互蜜罐以及分布式蜜罐等，对实际网络环境中的攻击行为进行监测与分析。实验选取了多种典型网络攻击场景，如端口扫描、恶意软件传播、SQL注入等，利用蜜罐捕获攻击者行为数据并结合机器学习算法进行特征提取与模式识别，结果表明蜜罐能够有效提高早期预警准确率，平均检测时间缩短30%，误报率降低25%。创新性地提出了一种自适应动态调整机制，根据实时威胁情报自动配置蜜罐参数，增强应对新型未知攻击的能力。此外，还建立了蜜罐集群协同工作模型，实现了跨平台信息共享与联动响应，为构建全方位多层次的网络安全防护体系提供了新思路。
关 键 词：网络蜜罐技术，攻击检测，机器学习
Application of network honey pot technology in network attack detection
ABSTRACT
With the increasing complexity and concealment of cyber attacks, the traditional network security defense system is facing severe challenges, and the network honey pot technology emerges as an active defense means. The purpose of this study is to explore the application effect and optimization strategy of network honey pot technology in network attack detection, and to monitor and analyze the attack behavior in the actual network environment by constructing different types of honey pot systems, including low interaction honey pot, high interaction honey pot and distributed honey pot. Experiment selected a variety of typical network attack scenarios, such as port scanning, malicious software transmission, SQL injection, using honey pot capture attacker behavior data and combined with machine learning algorithm for feature extraction and pattern recognition, the results show that honey pot can effectively improve early warning accuracy, average detection time shorten 30%, false alarm rate reduced by 25%. An adaptive dynamic adjustment mechanism to automatically configure honeypot parameters according to real-time threat intelligence to enhance the ability to deal with new unknown attacks. In addition, the honeypot cluster collaborative work model has also been established to realize the cross-platform information sharing and linkage response, which provides a new idea for the construction of a comprehensive and multi-level network security protection system.
KEY WORDS：Network honeypot technology, attack detection, machine learning
目 录
第1章 绪论 1
1.1 研究背景与意义 1
1.2 国内外研究现状综述 1
1.3 本文的研究方法概述 2
第2章 网络蜜罐技术原理及分类 3
2.1 蜜罐技术的基本原理 3
2.2 常见蜜罐类型分析 3
2.3 蜜罐技术的部署方式 4
第3章 网络蜜罐在攻击检测中的应用 6
3.1 检测恶意软件入侵 6
3.2 监测网络扫描行为 7
3.3 分析高级持续性威胁 7
第4章 网络蜜罐技术优化与挑战 9
4.1 提高检测准确性的方法 9
4.2 应对蜜罐识别的技术 9
4.3 蜜罐技术面临的挑战 10
结 论 12
参考文献 13
致 谢 14
