摘 要
随着信息技术的迅猛发展，恶意软件威胁日益严重，其中零日攻击因其未知性和突发性成为网络安全领域的重大挑战。为应对这一问题，本研究聚焦于零日攻击检测技术，旨在探索一种高效、准确且具有前瞻性的检测方法。通过深入分析现有恶意软件检测技术的局限性，特别是静态分析和动态分析在面对未知威胁时的不足，提出了一种基于行为特征与深度学习相结合的零日攻击检测框架。该框架首先利用系统调用序列提取程序运行时的行为特征，然后采用卷积神经网络对这些特征进行建模，从而实现对未知恶意行为的有效识别。实验结果表明，相较于传统检测方法，所提方法在检测率上提升了约20%，同时将误报率控制在较低水平。此外，该方法能够适应不同类型的恶意软件变种，展现出良好的泛化能力。本研究的主要创新点在于突破了传统检测依赖已知特征库的限制，从行为角度构建了一个更加灵活、智能的检测体系，为防范新型恶意软件提供了新的思路和技术手段，对提升网络安全防护水平具有重要意义。
关键词：零日攻击检测 行为特征提取 深度学习
Abstract
With the rapid development of information technology, the threat posed by malicious software has become increasingly severe, with zero-day attacks emerging as a significant challenge in cybersecurity due to their unpredictability and suddenness. To address this issue, this study focuses on zero-day attack detection techniques, aiming to explore an efficient, accurate, and forward-looking detection method. By thoroughly analyzing the limitations of existing malware detection technologies, particularly the inadequacies of static and dynamic analysis in confronting unknown threats, we propose a zero-day attack detection fr amework that integrates behavioral characteristics with deep learning. This fr amework first extracts behavioral features from system call sequences during program execution and then employs convolutional neural networks to model these features, thereby achieving effective identification of unknown malicious behaviors. Experimental results demonstrate that, compared to traditional detection methods, the proposed approach improves detection rates by approximately 20% while maintaining a low false positive rate. Additionally, this method exhibits strong generalization capabilities, adapting to various types of malware variants. The primary innovation of this research lies in overcoming the limitations of traditional detection methods that rely on known feature databases, constructing a more flexible and intelligent detection system from a behavioral perspective. This provides new ideas and technical means for defending against novel malware, significantly enhancing the level of cybersecurity protection.
Keyword:Zero-Day Attack Detection Behavior Feature Extraction Deep Learning
目 录
1绪论 1
1.1研究背景与意义 1
1.2零日攻击检测研究现状 1
1.3本文研究方法概述 2
2零日攻击特征分析 2
2.1攻击行为模式识别 2
2.2特征提取技术研究 3
2.3动态行为特征建模 3
3检测算法设计与优化 4
3.1基于机器学习的检测 4
3.2异常检测算法改进 5
3.3多模型融合策略研究 5
4实验验证与结果分析 6
4.1实验环境搭建 6
4.2检测性能评估 7
4.3结果对比与讨论 7
结论 8
参考文献 9
致谢 10
