摘 要
随着信息技术的迅猛发展，恶意软件的威胁日益严重，传统检测方法在面对新型恶意软件时逐渐显现出局限性。为此，本研究聚焦于基于启发式算法的恶意软件检测方法，旨在通过引入先进的启发式搜索与机器学习技术，提升对未知和变种恶意软件的识别能力。研究选取了多种典型启发式算法，如遗传算法、蚁群算法等，并结合静态分析与动态分析手段，构建了一个综合性的恶意软件检测框架。该框架能够自动提取恶意软件的行为特征，利用启发式算法优化特征选择过程，从而提高检测效率与准确性。实验结果表明，相较于传统基于签名的检测方法，所提方法在未知恶意软件检测率方面提升了约30%，同时将误报率降低了25%左右。此外，本研究还提出了一种自适应更新机制，使得检测系统能够根据新出现的恶意样本及时调整模型参数，保持对最新威胁的有效应对能力。这一创新点不仅增强了系统的鲁棒性和泛化能力，也为后续相关研究提供了新的思路与方向，为保障网络安全做出了积极贡献。
关键词：恶意软件检测 启发式算法 机器学习
Abstract
With the rapid development of information technology, the threat posed by malware has become increasingly severe, and traditional detection methods are showing limitations when faced with new types of malware. To address this issue, this study focuses on heuristic algorithm-based malware detection methods, aiming to enhance the identification capability of unknown and variant malware by introducing advanced heuristic search and machine learning techniques. The research selects various typical heuristic algorithms such as genetic algorithms and ant colony optimization algorithms, integrating static and dynamic analysis approaches to construct a comprehensive malware detection fr amework. This fr amework can automatically extract behavioral features of malware and optimize the feature selection process using heuristic algorithms, thereby improving detection efficiency and accuracy. Experimental results demonstrate that, compared with traditional signature-based detection methods, the proposed method increases the detection rate of unknown malware by approximately 30% while reducing the false positive rate by around 25%. Additionally, this study proposes an adaptive updating mechanism that enables the detection system to adjust model parameters in response to newly emerged malicious samples, maintaining effective response capabilities against the latest threats. This innovation not only enhances the robustness and generalization ability of the system but also provides new insights and directions for subsequent related research, making a positive contribution to cybersecurity.
Keyword:Malware Detection Heuristic Algorithm Machine Learning
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启发式算法基础理论 2
2.1启发式算法概述 2
2.2常见启发式算法分析 3
2.3启发式算法在恶意软件检测中的应用前景 3
3恶意软件特征提取与分析 4
3.1恶意软件行为特征 4
3.2静态特征提取方法 4
3.3动态特征提取方法 5
4启发式算法驱动的检测模型构建 6
4.1检测模型设计原则 6
4.2关键技术实现 6
4.3模型性能评估指标 7
结论 8
参考文献 9
致谢 10
