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，传统基于规则和签名的检测方法难以应对未知攻击和零日漏洞，深度学习算法凭借其强大的特征提取与模式识别能力为网络安全异常检测提供了新的思路。本研究旨在探索深度学习算法在网络安全异常检测中的应用效果并寻求更优解决方案。以卷积神经网络、循环神经网络等典型深度学习模型为基础，结合网络安全流量数据特点进行改进优化，构建适合异常检测任务的新模型架构。通过引入注意力机制增强对关键信息的关注度，并采用对抗训练提升模型鲁棒性。实验结果表明，所提方法在多种真实网络环境中均能有效提高异常检测准确率，降低误报率，尤其对于隐蔽性强、变化快的新型攻击具备更好的识别能力。该研究不仅验证了深度学习应用于网络安全领域的可行性，还为后续研究指明方向，创新性地将深度学习理论与网络安全实践深度融合，为构建更加智能高效的网络安全防护体系奠定坚实基础。
关键词：深度学习；网络安全异常检测；卷积神经网络
Abstract
With the rapid development of information technology, cybersecurity threats have become increasingly complex and dynamic. Traditional detection methods based on rules and signatures struggle to address unknown attacks and zero-day vulnerabilities. Deep learning algorithms, with their powerful feature extraction and pattern recognition capabilities, offer a new approach to cybersecurity anomaly detection. This study aims to explore the effectiveness of deep learning algorithms in cybersecurity anomaly detection and seeks optimal solutions. By leveraging typical deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), and adapting them to the characteristics of cybersecurity traffic data, we propose an improved model architecture tailored for anomaly detection tasks. The introduction of attention mechanisms enhances the focus on critical information, while adversarial training improves model robustness. Experimental results demonstrate that the proposed method effectively increases anomaly detection accuracy and reduces false positive rates across various real-world network environments, particularly excelling in identifying novel attacks with strong stealth and rapid changes. This research not only validates the feasibility of applying deep learning to the field of cybersecurity but also provides direction for future studies, innovatively integrating deep learning theory with cybersecurity practices to establish a more intelligent and efficient cybersecurity protection system.
Keywords：Deep Learning; Network Security Anomaly 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) 研究方法概述 2
二、深度学习算法基础 2
(一) 深度学习基本原理 2
(二) 常用深度学习模型 3
(三) 模型在网络安全中的适配性 4
三、异常检测技术分析 5
(一) 异常检测定义与分类 5
(二) 传统异常检测方法 6
(三) 深度学习驱动的检测优势 6
四、应用案例与效果评估 7
(一) 实际应用场景介绍 7
(二) 检测性能评估指标 8
(三) 案例分析与结果讨论 9
结 论 10
参考文献 11
随着信息技术的迅猛发展，网络安全威胁日益复杂多变，传统基于规则和签名的检测方法难以应对未知攻击和零日漏洞，深度学习算法凭借其强大的特征提取与模式识别能力为网络安全异常检测提供了新的思路。本研究旨在探索深度学习算法在网络安全异常检测中的应用效果并寻求更优解决方案。以卷积神经网络、循环神经网络等典型深度学习模型为基础，结合网络安全流量数据特点进行改进优化，构建适合异常检测任务的新模型架构。通过引入注意力机制增强对关键信息的关注度，并采用对抗训练提升模型鲁棒性。实验结果表明，所提方法在多种真实网络环境中均能有效提高异常检测准确率，降低误报率，尤其对于隐蔽性强、变化快的新型攻击具备更好的识别能力。该研究不仅验证了深度学习应用于网络安全领域的可行性，还为后续研究指明方向，创新性地将深度学习理论与网络安全实践深度融合，为构建更加智能高效的网络安全防护体系奠定坚实基础。
关键词：深度学习；网络安全异常检测；卷积神经网络
Abstract
With the rapid development of information technology, cybersecurity threats have become increasingly complex and dynamic. Traditional detection methods based on rules and signatures struggle to address unknown attacks and zero-day vulnerabilities. Deep learning algorithms, with their powerful feature extraction and pattern recognition capabilities, offer a new approach to cybersecurity anomaly detection. This study aims to explore the effectiveness of deep learning algorithms in cybersecurity anomaly detection and seeks optimal solutions. By leveraging typical deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), and adapting them to the characteristics of cybersecurity traffic data, we propose an improved model architecture tailored for anomaly detection tasks. The introduction of attention mechanisms enhances the focus on critical information, while adversarial training improves model robustness. Experimental results demonstrate that the proposed method effectively increases anomaly detection accuracy and reduces false positive rates across various real-world network environments, particularly excelling in identifying novel attacks with strong stealth and rapid changes. This research not only validates the feasibility of applying deep learning to the field of cybersecurity but also provides direction for future studies, innovatively integrating deep learning theory with cybersecurity practices to establish a more intelligent and efficient cybersecurity protection system.
Keywords：Deep Learning; Network Security Anomaly 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) 研究方法概述 2
二、深度学习算法基础 2
(一) 深度学习基本原理 2
(二) 常用深度学习模型 3
(三) 模型在网络安全中的适配性 4
三、异常检测技术分析 5
(一) 异常检测定义与分类 5
(二) 传统异常检测方法 6
(三) 深度学习驱动的检测优势 6
四、应用案例与效果评估 7
(一) 实际应用场景介绍 7
(二) 检测性能评估指标 8
(三) 案例分析与结果讨论 9
结 论 10
参考文献 11
