摘 要
随着信息技术的迅猛发展，网络安全威胁日益严峻，传统基于规则和特征匹配的网络攻击检测方法难以应对复杂多变的攻击模式。本研究旨在构建基于深度学习的网络攻击检测与防御系统，以提高检测效率和准确性。通过引入卷积神经网络、循环神经网络等深度学习模型，结合大数据分析技术，对海量网络流量数据进行实时监测与异常行为识别。创新性地提出了一种融合多源异构数据的特征提取方法，有效提升了模型的泛化能力。实验结果表明，该系统在检测准确率、召回率等方面均优于传统方法，特别是在处理新型未知攻击时表现出色。此外，针对误报率高的问题，设计了自适应阈值调整机制，显著降低了误报率。
关键词：网络安全威胁 深度学习 网络攻击检测
Abstract
With the rapid development of information technology, the threat of network security is increasingly serious, and the traditional network attack detection methods based on rules and feature matching are difficult to deal with the complex and changeable attack patterns. This study aims to construct a deep learning-based cyberattack detection and defense system to improve detection efficiency and accuracy. By introducing deep learning models such as convolutional neural network and recurrent neural network, combined with big data analysis technology, real-time monitoring of massive network traffic data and abnormal behavior identification are conducted. A feature extraction method integrating multi-source heterogeneous data is innovatively proposed, which effectively improves the generalization ability of the model. The experimental results show that the system outperforms the conventional methods in detection accuracy and recall, especially in dealing with novel unknown attacks. Moreover, an adaptive threshold adjustment mechanism was designed to address the high false alarm rate, which significantly reduced the false alarm rate.
Keyword:Cybersecurity Threat Deep Learning Network Attack Detection
目 录
1绪论 1
1.1网络攻击检测的背景与意义 1
1.2国内外研究现状综述 1
1.3本文研究方法概述 1
2深度学习技术在网络安全中的应用 2
2.1深度学习基础理论 2
2.2深度学习模型在网络攻击检测中的应用 2
2.3深度学习算法优化与改进 3
3基于深度学习的网络攻击检测系统设计 4
3.1系统架构设计原则 4
3.2数据预处理与特征提取 5
3.3攻击检测模型构建 5
4基于深度学习的防御策略研究 6
4.1防御机制原理分析 6
4.2实时响应与阻断技术 6
4.3防御效果评估体系 7
结论 8
参考文献 9
致谢 10
随着信息技术的迅猛发展，网络安全威胁日益严峻，传统基于规则和特征匹配的网络攻击检测方法难以应对复杂多变的攻击模式。本研究旨在构建基于深度学习的网络攻击检测与防御系统，以提高检测效率和准确性。通过引入卷积神经网络、循环神经网络等深度学习模型，结合大数据分析技术，对海量网络流量数据进行实时监测与异常行为识别。创新性地提出了一种融合多源异构数据的特征提取方法，有效提升了模型的泛化能力。实验结果表明，该系统在检测准确率、召回率等方面均优于传统方法，特别是在处理新型未知攻击时表现出色。此外，针对误报率高的问题，设计了自适应阈值调整机制，显著降低了误报率。
关键词：网络安全威胁 深度学习 网络攻击检测
Abstract
With the rapid development of information technology, the threat of network security is increasingly serious, and the traditional network attack detection methods based on rules and feature matching are difficult to deal with the complex and changeable attack patterns. This study aims to construct a deep learning-based cyberattack detection and defense system to improve detection efficiency and accuracy. By introducing deep learning models such as convolutional neural network and recurrent neural network, combined with big data analysis technology, real-time monitoring of massive network traffic data and abnormal behavior identification are conducted. A feature extraction method integrating multi-source heterogeneous data is innovatively proposed, which effectively improves the generalization ability of the model. The experimental results show that the system outperforms the conventional methods in detection accuracy and recall, especially in dealing with novel unknown attacks. Moreover, an adaptive threshold adjustment mechanism was designed to address the high false alarm rate, which significantly reduced the false alarm rate.
Keyword:Cybersecurity Threat Deep Learning Network Attack Detection
目 录
1绪论 1
1.1网络攻击检测的背景与意义 1
1.2国内外研究现状综述 1
1.3本文研究方法概述 1
2深度学习技术在网络安全中的应用 2
2.1深度学习基础理论 2
2.2深度学习模型在网络攻击检测中的应用 2
2.3深度学习算法优化与改进 3
3基于深度学习的网络攻击检测系统设计 4
3.1系统架构设计原则 4
3.2数据预处理与特征提取 5
3.3攻击检测模型构建 5
4基于深度学习的防御策略研究 6
4.1防御机制原理分析 6
4.2实时响应与阻断技术 6
4.3防御效果评估体系 7
结论 8
参考文献 9
致谢 10
