摘 要
随着信息技术的迅猛发展，恶意软件威胁日益严重且变种形式层出不穷，传统检测方法难以应对新型变种的快速演变。为此，本研究聚焦于恶意软件检测中的变种识别技术，旨在构建一种高效、准确且具有泛化能力的变种识别系统。研究基于静态分析与动态分析相结合的方法，提出了一种融合多特征提取机制的深度学习框架，该框架能够从代码结构、行为模式等多个维度全面表征恶意软件样本特征。通过引入对抗生成网络优化模型训练过程，有效提升了对未知变种的识别率。实验结果表明，在包含大量已知及模拟变种样本的数据集上，所提方法相较于传统检测算法在查全率和查准率方面均有显著提升，特别是在面对高度变异样本时表现出更强的鲁棒性。本研究不仅为恶意软件变种识别提供了新的思路和技术手段，而且其创新性的多特征融合与对抗训练机制也为其他领域内相似问题的研究提供了有益借鉴。
关键词：恶意软件变种识别，多特征融合，深度学习框架
ABSTRACT
With the rapid development of information technology, the threat of malware is becoming more and more serious and variant forms emerge endlessly. Traditional detection methods cannot cope with the rapid evolution of new variants. Therefore, this research focuses on variant recognition technology in malware detection, aiming to build an efficient, accurate and generalized variant recognition system. Based on the method of combining static analysis and dynamic analysis, this paper proposes a deep learning fr amework integrating multi-feature extraction mechanism, which can comprehensively characterize the features of malware samples from multiple dimensions such as code structure and behavior pattern. By introducing the training process of adversarial generation network optimization model, the recognition rate of unknown variants is effectively improved. The experimental results show that compared with traditional detection algorithms, the proposed method has significantly improved recall rate and precision rate on a dataset containing a large number of known and simulated variant samples, especially in the face of highly variable samples, showing stronger robustness. This research not only provides a new idea and technical means for malware variant recognition, but also provides a useful reference for the research of similar problems in other fields with its innovative multi-feature fusion and antagonistic training mechanism.
Keywords: Malware variant recognition, Multi-feature fusion, Deep learning fr amework
目 录
摘 要 I
ABSTRACT II
第一章 绪论 1
1.1 研究背景及意义 1
1.2 国内外研究现状综述 1
第二章 恶意软件变种特征分析 3
2.1 变种恶意软件的演变规律 3
2.2 关键特征提取方法探讨 3
2.3 特征稳定性与变异性的平衡 4
第三章 变种识别算法设计与优化 6
3.1 基于静态分析的识别算法 6
3.2 动态行为模式识别技术 6
3.3 算法性能评估与改进策略 7
第四章 实验验证与应用案例 9
4.1 实验环境与数据集构建 9
4.2 不同场景下的识别效果 9
4.3 实际应用中的挑战与应对 10
结束语 12
谢 辞 13
参考文献 14
