摘 要
随着网络攻击手段的不断升级，传统防御机制在应对复杂威胁时逐渐显现出局限性，蜜罐技术作为一种主动防御策略，为网络安全研究提供了新的思路。本研究旨在深入探讨蜜罐技术的核心原理及其在实际场景中的应用价值，通过构建多层次、智能化的蜜罐系统，实现对攻击行为的诱导、捕获与分析。研究采用理论分析与实验验证相结合的方法，首先对现有蜜罐技术进行分类梳理，明确高交互蜜罐与低交互蜜罐的特点及适用范围；其次设计并实现了一种基于机器学习的动态蜜罐模型，该模型能够根据攻击特征自适应调整伪装策略，从而提升诱骗效果和数据采集质量。实验结果表明，所提出的动态蜜罐模型在攻击检测率和数据真实性方面均优于传统静态蜜罐，同时显著降低了系统的资源消耗。此外，通过对捕获数据的深度分析，揭示了新型攻击模式及潜在威胁来源，为网络安全防护体系的优化提供了重要参考。本研究的主要创新点在于将机器学习引入蜜罐技术，实现了蜜罐行为的智能化调整，并首次提出了一种结合蜜罐数据与日志信息的综合威胁评估方法。研究成果不仅丰富了蜜罐技术的理论框架，还为实际部署提供了可行方案，对于提升网络安全防御能力具有重要意义。
关键词：蜜罐技术；机器学习；动态蜜罐模型；攻击行为分析；威胁评估方法
Abstract
With the continuous evolution of network attack methods, traditional defense mechanisms are increasingly revealing limitations in addressing complex threats. Honey pot technology, as a form of active defense strategy, offers new perspectives for cybersecurity research. This study aims to explore the core principles of honey pot technology and its application value in practical scenarios by constructing a multi-layered, intelligent honey pot system to induce, capture, and analyze attack behaviors. A combination of theoretical analysis and experimental validation is employed. First, existing honey pot technologies are classified and reviewed to clarify the characteristics and applicable scopes of high-interaction and low-interaction honey pots. Second, a dynamic honey pot model based on machine learning is designed and implemented. This model can adaptively adjust its disguise strategies according to attack features, thereby enhancing deception effectiveness and data collection quality. Experimental results demonstrate that the proposed dynamic honey pot model outperforms traditional static honey pots in terms of attack detection rates and data authenticity while significantly reducing system resource consumption. Furthermore, in-depth analysis of captured data reveals novel attack patterns and potential threat sources, providing critical references for optimizing cybersecurity protection systems. The primary innovation of this study lies in integrating machine learning into honey pot technology to achieve intelligent adjustments of honey pot behavior and proposing, for the first time, a comprehensive threat evaluation method that combines honey pot data with log information. The research not only enriches the theoretical fr amework of honey pot technology but also provides feasible solutions for practical deployment, playing a significant role in enhancing cybersecurity defense capabilities.
Keywords: Honeypot Technology; Machine Learning; Dynamic Honeypot Model; Attack Behavior Analysis; Threat Assessment Method
目 录
1绪论 1
1.1网络安全与蜜罐技术背景 1
1.2蜜罐技术研究的意义与价值 1
1.3国内外蜜罐技术研究现状分析 1
1.4本文研究方法与技术路线 2
2蜜罐技术基础理论与分类 2
2.1蜜罐技术的基本概念与原理 2
2.2蜜罐技术的主要类型与特点 3
2.3主动蜜罐与被动蜜罐的功能对比 3
2.4高交互蜜罐与低交互蜜罐的应用场景 4
2.5蜜罐技术在网络安全中的角色定位 4
3蜜罐技术的设计与实现 4
3.1蜜罐系统架构设计原则 5
3.2数据捕获与日志记录机制分析 5
3.3虚拟化技术在蜜罐中的应用实践 6
3.4蜜罐系统的部署与配置优化策略 6
3.5蜜罐技术的安全性与性能权衡 7
4蜜罐技术的实际应用与案例分析 7
4.1蜜罐技术在网络攻击检测中的应用 7
4.2蜜罐在APT攻击防御中的作用研究 8
4.3工业控制系统中蜜罐技术的实施案例 8
4.4蜜罐技术在物联网安全中的创新应用 9
4.5蜜罐技术未来发展趋势与挑战 9
结论 9
参考文献 11
致 谢 12
