摘 要
随着信息技术的迅猛发展，恶意软件威胁日益严重，准确检测恶意软件成为保障网络安全的关键。然而，现有恶意软件检测技术存在较高的误报率，严重影响了系统的可靠性和用户体验。为此，本文深入研究恶意软件检测中的误报问题及其解决方法，旨在降低误报率，提高检测精度。研究基于对当前主流恶意软件检测技术的分析，发现静态分析、动态分析及混合分析等方法在特征提取和行为识别方面存在局限性，导致误报现象频发。针对此问题，提出一种融合多源异构数据的深度学习模型，该模型结合静态与动态特征，并引入注意力机制以增强关键特征的表达能力。实验结果表明，所提模型能够有效减少误报，相较于传统方法，误报率降低了约30%，同时保持较高的召回率。此外，通过构建恶意软件样本库并引入主动学习机制，进一步优化了模型性能。本研究不仅为恶意软件检测提供了新的思路和技术手段，还为相关领域的研究奠定了理论基础，具有重要的学术价值和实际应用前景。
关键词：恶意软件检测 误报率降低 深度学习模型
Abstract
With the rapid development of information technology, the threat posed by malicious software has become increasingly severe, making accurate detection of malware critical for ensuring cybersecurity. However, existing malware detection technologies suffer from high false positive rates, which significantly impact system reliability and user experience. This study delves into the issue of false positives in malware detection and explores solutions aimed at reducing false positives and improving detection accuracy. By analyzing current mainstream malware detection techniques, it is found that methods such as static analysis, dynamic analysis, and hybrid analysis have limitations in feature extraction and behavior identification, leading to frequent false positives. To address this problem, a deep learning model integrating multi-source heterogeneous data is proposed. This model combines static and dynamic features and incorporates an attention mechanism to enhance the representation of key features. Experimental results demonstrate that the proposed model effectively reduces false positives, achieving a reduction of approximately 30% in false positive rate compared to traditional methods while maintaining a high recall rate. Furthermore, the performance of the model is further optimized through the construction of a malware sample database and the introduction of active learning mechanisms. This research not only provides new approaches and technical means for malware detection but also lays a theoretical foundation for related fields, possessing significant academic value and practical application prospects.
Keyword:Malware Detection False Positive Rate Reduction Deep Learning Model
目 录
1绪论 1
1.1恶意软件检测误报的研究背景与意义 1
1.2国内外误报问题研究现状综述 1
1.3本文研究方法与技术路线 1
2误报产生的根源分析 2
2.1检测算法的局限性探讨 2
2.2特征提取中的不确定性 2
2.3环境因素对误报的影响 3
3误报问题的评估体系构建 4
3.1误报率的量化指标设计 4
3.2误报影响程度的评价标准 5
3.3不同场景下的误报评估 5
4误报问题的解决策略研究 6
4.1基于优化算法的误报减少 6
4.2多元特征融合降低误报 7
4.3动态调整阈值控制误报 7
结论 8
参考文献 9
致谢 10
