摘 要
随着网络技术的快速发展和信息化程度的不断提高，恶意软件已成为网络安全领域的重要威胁之一，而多态性恶意软件因其能够通过频繁变换代码特征逃避传统检测机制的特点，进一步加剧了检测难度。本研究旨在深入探讨多态性恶意软件的生成机制及其对现有检测技术的挑战，并提出一种基于深度学习与行为分析相结合的新型检测框架以应对这一问题。具体而言，该研究首先通过逆向工程分析了多态性恶意软件的核心特性，包括加密算法、变异引擎及混淆技术等关键要素，随后设计了一种融合静态特征提取与动态行为监控的综合检测模型。此模型利用卷积神经网络对恶意代码的静态图像化表示进行高效特征提取，同时结合长短期记忆网络捕捉程序运行时的行为序列特征，从而实现对多态性恶意软件的精准识别。实验结果表明，相较于传统的签名匹配和启发式分析方法，所提出的框架在检测准确率和泛化能力方面均有显著提升，特别是在面对未知变种时表现出更强的适应性。此外，本研究还开发了一套自动化样本生成与验证工具，用于评估检测系统的鲁棒性和实时性。总体而言，本研究不仅揭示了多态性恶意软件的本质特征，还为构建下一代智能化检测系统提供了理论支持和技术路径，其创新点在于将深度学习与行为分析有机融合，为解决多态性问题提供了全新的思路。
关 键 词：多态性恶意软件，深度学习，行为分析，检测框架，卷积神经网络，长短期记忆网络
ABSTRACT
With the rapid development of network technology and the continuous improvement of informatization, malicious software has become one of the significant threats in the field of cybersecurity. Polymorphic malware, in particular, poses an even greater challenge due to its ability to evade traditional detection mechanisms by frequently altering its code characteristics. This study aims to explore the generation mechanisms of polymorphic malware and the challenges it presents to existing detection technologies, proposing a novel detection fr amework that combines deep learning with behavioral analysis to address this issue. Specifically, the research first analyzes the core characteristics of polymorphic malware through reverse engineering, focusing on key elements such as encryption algorithms, mutation engines, and obfuscation techniques. Subsequently, a comprehensive detection model integrating static feature extraction and dynamic behavior monitoring is designed. This model employs convolutional neural networks for efficient feature extraction from the static image representation of malicious code while leveraging long short-term memory networks to capture behavioral sequence features during program execution, thereby achieving precise identification of polymorphic malware. Experimental results demonstrate that, compared to traditional signature matching and heuristic analysis methods, the proposed fr amework significantly enhances detection accuracy and generalization capability, particularly exhibiting stronger adaptability when confronting unknown variants. Additionally, this study develops an automated sample generation and validation toolkit to evaluate the robustness and real-time performance of the detection system. Overall, this research not only reveals the essential characteristics of polymorphic malware but also provides theoretical support and technical pathways for constructing next-generation intelligent detection systems. Its innovation lies in the organic integration of deep learning and behavioral analysis, offering a new approach to resolving polymorphism-related issues.
KEY WORDS：Polymorphic Malware, Deep Learning, Behavior Analysis, Detection fr amework, Convolutional Neural Network, Long Short-Term Memory Network
目 录
第1章 绪论 1
1.1 恶意软件检测的背景与意义 1
1.2 多态性问题的研究现状分析 1
1.3 本文研究方法概述 2
第2章 多态性技术原理与挑战 3
2.1 多态性技术的基本概念 3
2.2 多态性在恶意软件中的应用形式 3
2.3 多态性对传统检测方法的挑战 4
2.4 当前多态性技术的发展趋势 4
2.5 多态性问题的核心难点 5
第3章 基于特征的多态性检测方法研究 7
3.1 特征提取技术的局限性分析 7
3.2 动态特征检测的实现机制 7
3.3 静态特征检测的优化策略 8
3.4 特征匹配算法的改进方向 8
3.5 特征检测方法的实际效果评估 8
第4章 新兴技术在多态性检测中的应用 10
4.1 机器学习在多态性检测中的潜力 10
4.2 深度学习模型的设计与优化 10
4.3 行为分析技术的应用场景 10
4.4 虚拟化技术在检测中的作用 11
4.5 新兴技术的综合比较与选择 11
结 论 12
参考文献 13
致 谢 14
