摘 要
随着网络攻击手段的不断演进，恶意软件检测技术面临着实时性与准确性难以兼顾的核心矛盾。传统基于特征码的检测方法在面对新型变种恶意软件时表现出滞后性，而依赖复杂分析的高级检测机制又往往导致响应延迟，影响系统性能。为此，本文旨在探索并构建一种能够在保证较高检测准确率的同时满足实时响应需求的恶意软件检测框架。研究提出了一种动态权衡模型，结合轻量级静态特征提取与选择性动态行为分析，在减少冗余计算的前提下提升整体检测效率。实验采用公开数据集与真实环境流量相结合的方式，评估不同策略下系统的响应时间与检测准确率表现。结果表明，相较传统单一检测机制，该方法在保持98%以上平均准确率的同时，将平均检测延迟降低至150毫秒以内。本研究不仅揭示了实时性与准确性之间的非线性关系，还为构建高效、智能的恶意软件防御体系提供了新的思路与实践路径。
关键词：恶意软件检测；动态权衡模型；实时性与准确性；轻量级静态特征；选择性动态行为分析
ABSTRACT
With the continuous evolution of cyber-attack techniques, malware detection technologies face a core contradiction between real-time performance and accuracy. Traditional signature-based detection methods exhibit latency when confronting new malware variants, while advanced detection mechanisms relying on complex analysis often lead to response delays and degrade system performance. To address this challenge, this study aims to explore and construct a malware detection fr amework capable of achieving both high detection accuracy and real-time responsiveness. A dynamic trade-off model is proposed, integrating lightweight static feature extraction with selective dynamic behavioral analysis, thereby enhancing overall detection efficiency without redundant computation. The experiments evaluate the system’s response time and detection accuracy under various strategies by combining publicly available datasets with real-world network traffic. Results demonstrate that, compared to conventional single-detection mechanisms, the proposed approach maintains an average accuracy of over 98% while reducing the average detection latency to within 150 milliseconds. This research not only reveals the nonlinear relationship between timeliness and accuracy but also provides new insights and practical pathways for building efficient and intelligent malware defense systems.
Keywords: Malware Detection; Dynamic Trade-off Model; Real-time And Accuracy; Lightweight Static Features; Selective Dynamic Behavior Analysis
目 录
摘 要 I
ABSTRACT II
第一章 绪论 1
1.1 研究背景和意义 1
1.2 领域研究现状 1
1.3 研究方法 1
第二章 恶意软件检测中实时性与准确性的基本关系 2
2.1 实时性对恶意软件检测系统的影响 2
2.2 准确性在恶意软件识别中的核心作用 2
2.3 实时性与准确性之间的冲突机制分析 3
2.4 权衡问题在不同检测架构中的体现 3
第三章 恶意软件检测技术对实时性与准确性的响应能力 5
3.1 基于签名的检测方法在权衡中的表现 5
3.2 启发式与行为分析技术的实时性挑战 5
3.3 机器学习模型在提升准确性中的应用 6
3.4 混合检测策略对权衡问题的缓解效果 6
第四章 提升恶意软件检测系统综合性能的优化路径 7
4.1 资源调度机制对实时性与准确性的影响 7
4.2 特征选择与数据预处理对检测效率的作用 7
4.3 动态调整检测策略以实现自适应平衡 8
4.4 基于反馈机制的系统性能持续优化方法 8
结 论 9
参考文献 10
致 谢 11
