摘要
随着信息技术的迅猛发展，网络安全威胁日益复杂多变，传统基于规则和特征匹配的安全检测方法难以应对新型攻击手段。为此，本研究旨在构建基于深度学习的网络安全威胁检测模型，以提高对未知威胁的识别能力。研究采用卷积神经网络与长短时记忆网络相结合的方法，针对网络流量数据进行特征提取与模式识别。通过对大规模真实网络环境下的流量样本进行训练，模型能够有效捕捉网络行为中的细微变化并实现对多种类型攻击的精准分类。实验结果表明，该模型在检测准确率、召回率以及处理速度等方面均优于现有主流方法，特别是在面对零日攻击等新型威胁时展现出更强的适应性。本研究创新性地引入注意力机制优化网络结构，使模型可以自动聚焦关键特征，显著提升了检测性能。此外，提出了一种基于自适应阈值的动态调整策略，进一步增强了系统的鲁棒性和实用性，为网络安全领域提供了新的技术思路与解决方案。
关键词：深度学习；网络安全威胁检测；卷积神经网络
Abstract
With the rapid development of information technology, cybersecurity threats have become increasingly complex and dynamic, rendering traditional rule-based and signature-matching security detection methods inadequate for addressing emerging attack vectors. This study aims to construct a deep learning-based cybersecurity threat detection model to enhance the identification of unknown threats. By integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks, this research focuses on feature extraction and pattern recognition from network traffic data. Trained on large-scale real-world network traffic samples, the model effectively captures subtle changes in network behavior and achieves precise classification of various types of attacks. Experimental results demonstrate that the proposed model outperforms existing mainstream methods in terms of detection accuracy, recall rate, and processing speed, particularly showing stronger adaptability to novel threats such as zero-day attacks. Innovatively, this study introduces an attention mechanism to optimize network architecture, enabling the model to automatically focus on critical features and significantly improving detection performance. Additionally, an adaptive threshold-based dynamic adjustment strategy is proposed, further enhancing the robustness and practicality of the system, thereby providing new technical insights and solutions for the field of cybersecurity.
Keywords：Deep Learning; Network Security Threat Detection; Convolutional Neural Network
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状 1
(三) 研究方法与技术路线 2
二、深度学习理论基础 2
(一) 深度学习基本原理 2
(二) 常用深度学习算法 3
(三) 深度学习在网络安全中的应用 4
(四) 深度学习模型优化方法 4
三、网络安全威胁检测需求分析 5
(一) 网络安全威胁类型 5
(二) 传统检测方法局限性 5
(三) 深度学习的优势与挑战 6
(四) 检测模型性能指标 7
四、基于深度学习的检测模型构建 7
(一) 数据预处理与特征提取 7
(二) 模型架构设计与选择 8
(三) 模型训练与参数调优 9
(四) 模型评估与验证方法 9
结 论 11
参考文献 12
随着信息技术的迅猛发展，网络安全威胁日益复杂多变，传统基于规则和特征匹配的安全检测方法难以应对新型攻击手段。为此，本研究旨在构建基于深度学习的网络安全威胁检测模型，以提高对未知威胁的识别能力。研究采用卷积神经网络与长短时记忆网络相结合的方法，针对网络流量数据进行特征提取与模式识别。通过对大规模真实网络环境下的流量样本进行训练，模型能够有效捕捉网络行为中的细微变化并实现对多种类型攻击的精准分类。实验结果表明，该模型在检测准确率、召回率以及处理速度等方面均优于现有主流方法，特别是在面对零日攻击等新型威胁时展现出更强的适应性。本研究创新性地引入注意力机制优化网络结构，使模型可以自动聚焦关键特征，显著提升了检测性能。此外，提出了一种基于自适应阈值的动态调整策略，进一步增强了系统的鲁棒性和实用性，为网络安全领域提供了新的技术思路与解决方案。
关键词：深度学习；网络安全威胁检测；卷积神经网络
Abstract
With the rapid development of information technology, cybersecurity threats have become increasingly complex and dynamic, rendering traditional rule-based and signature-matching security detection methods inadequate for addressing emerging attack vectors. This study aims to construct a deep learning-based cybersecurity threat detection model to enhance the identification of unknown threats. By integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks, this research focuses on feature extraction and pattern recognition from network traffic data. Trained on large-scale real-world network traffic samples, the model effectively captures subtle changes in network behavior and achieves precise classification of various types of attacks. Experimental results demonstrate that the proposed model outperforms existing mainstream methods in terms of detection accuracy, recall rate, and processing speed, particularly showing stronger adaptability to novel threats such as zero-day attacks. Innovatively, this study introduces an attention mechanism to optimize network architecture, enabling the model to automatically focus on critical features and significantly improving detection performance. Additionally, an adaptive threshold-based dynamic adjustment strategy is proposed, further enhancing the robustness and practicality of the system, thereby providing new technical insights and solutions for the field of cybersecurity.
Keywords：Deep Learning; Network Security Threat Detection; Convolutional Neural Network
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状 1
(三) 研究方法与技术路线 2
二、深度学习理论基础 2
(一) 深度学习基本原理 2
(二) 常用深度学习算法 3
(三) 深度学习在网络安全中的应用 4
(四) 深度学习模型优化方法 4
三、网络安全威胁检测需求分析 5
(一) 网络安全威胁类型 5
(二) 传统检测方法局限性 5
(三) 深度学习的优势与挑战 6
(四) 检测模型性能指标 7
四、基于深度学习的检测模型构建 7
(一) 数据预处理与特征提取 7
(二) 模型架构设计与选择 8
(三) 模型训练与参数调优 9
(四) 模型评估与验证方法 9
结 论 11
参考文献 12
