摘 要
随着信息技术的迅猛发展，网络安全威胁日益复杂化和多样化，传统检测方法在面对高级持续性威胁（APT）和零日漏洞时逐渐显现出局限性。本研究旨在探讨人工智能技术在网络安全威胁检测中的应用潜力与挑战，并提出一种基于深度学习的多模态威胁检测框架。通过整合行为分析、流量监测和异常检测等多源数据，该框架能够有效识别未知威胁并提升检测精度。研究采用卷积神经网络（CNN）与长短期记忆网络（LSTM）相结合的方法，对大规模网络安全数据集进行训练与验证，结果表明，相较于传统规则匹配和浅层机器学习方法，所提框架在检测率和误报率方面均表现出显著优势。此外，研究还深入分析了人工智能在网络安全领域面临的挑战，包括模型可解释性不足、对抗样本攻击风险以及数据隐私保护等问题。
关键词：人工智能；网络安全威胁检测；深度学习；多模态数据；对抗样本攻击
ABSTRACT
With the rapid development of information technology, network security threats are increasingly complex and diversified, and traditional detection methods gradually show limitations in the face of advanced persistent threats (APT) and zero-day vulnerabilities. This study aims to explore the application potential and challenges of AI technology in cybersecurity threat detection and to propose a deep learning-based fr amework for multimodal threat detection. By integrating multi-source data such as behavior analysis, traffic monitoring and anomaly detection, this fr amework can effectively identify unknown threats and improve detection accuracy. The study used convolutional neural network (CNN) and long-term memory network (LSTM) to train and validate the large-scale network security data set. The results show that compared with the traditional rule matching and shallow machine learning methods, the proposed fr amework shows significant advantages in terms of detection rate and false alarm rate. In addition, the research also deeply analyzes the challenges faced by AI in the field of network security, including the insufficient model interpretability, the risk of resisting sample attacks, and data privacy protection.
KEY WORDS： Artificial Intelligence;Cybersecurity Threat Detection;Deep Learning;Multimodal Data;Adversarial Sample Attack
目 录
摘 要 I
ABSTRACT II
1 绪论 1
1.1 网络安全威胁检测的背景与意义 1
1.2 人工智能在威胁检测中的研究现状 1
1.3 本文研究方法与技术路线 2
2 人工智能在威胁检测中的关键技术 2
2.1 数据采集与预处理方法 3
2.2 基于机器学习的威胁建模 3
2.3 深度学习在网络攻击识别中的应用 4
2.4 异常检测算法的设计与优化 5
2.5 关键技术的性能评估与比较 6
3 人工智能驱动的威胁检测系统设计 6
3.1 系统架构与功能模块划分 6
3.2 实时威胁检测机制的实现 7
3.3 数据流管理与资源分配策略 7
3.4 可扩展性与兼容性设计分析 8
3.5 系统性能测试与结果分析 9
4 人工智能在威胁检测中的挑战与应对策略 9
4.1 数据隐私保护的技术难题 9
4.2 模型可解释性与透明度问题 10
4.3 对抗样本攻击的风险与防范 11
4.4 高维度数据处理的效率瓶颈 11
4.5 多源异构数据融合的挑战与解决路径 12
结论 13
致 谢 14
参考文献 15
