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，传统的安全防护手段已难以满足需求，基于深度神经网络构建网络安全态势感知模型成为应对这一挑战的有效途径。本研究旨在建立一种高效准确的网络安全态势感知模型，以实现对网络环境中潜在威胁的实时监测与预警。为此，提出了一种融合多种深度学习算法的创新架构，该架构结合了卷积神经网络（CNN）用于特征提取、循环神经网络（RNN）处理时间序列数据以及自注意力机制优化信息传递路径。通过引入对抗训练机制提升模型鲁棒性，并采用半监督学习方法解决标注数据不足的问题。实验结果表明，在多个公开数据集上，所提模型相较于传统方法及现有先进模型，在检测率、误报率等关键性能指标方面均有显著提升，特别是在面对新型未知攻击时展现出更强的适应能力。本研究不仅为网络安全领域提供了新的技术思路，还推动了深度学习理论在实际应用中的进一步发展，对于构建更加智能可靠的网络防御体系具有重要意义。
关键词：网络安全态势感知；深度神经网络；卷积神经网络
Abstract
With the rapid development of information technology, cyber security threats have become increasingly complex and dynamic, rendering traditional security protection methods inadequate. To address this challenge, constructing a cyber security situational awareness model based on deep neural networks has emerged as an effective approach. This study aims to establish an efficient and accurate cyber security situational awareness model for real-time monitoring and early warning of potential threats in network environments. To achieve this, an innovative architecture integrating multiple deep learning algorithms is proposed, combining Convolutional Neural Networks (CNN) for feature extraction, Recurrent Neural Networks (RNN) for processing time-series data, and self-attention mechanisms to optimize information transmission paths. The robustness of the model is enhanced by incorporating adversarial training, while semi-supervised learning addresses the issue of insufficient labeled data. Experimental results demonstrate that, across multiple public datasets, the proposed model significantly outperforms traditional methods and existing advanced models in key performance metrics such as detection rate and false positive rate, particularly exhibiting stronger adaptability to novel and unknown attacks. This research not only provides new technical insights for the field of cyber security but also advances the practical application of deep learning theory, contributing significantly to the development of more intelligent and reliable network defense systems.
Keywords：Cybersecurity Situation Awareness; Deep Neural Network; 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、深度神经网络基础理论 2
(一) 深度学习基本原理 2
(二) 常用深度神经网络架构 3
(三) 网络安全中的应用挑战 4
三、数据采集与预处理技术 5
(一) 多源数据融合机制 5
(二) 异常流量特征提取 5
(三) 数据标注与质量控制 6
四、态势感知模型构建与优化 7
(一) 模型架构设计原则 7
(二) 关键算法选择依据 8
(三) 模型性能评估指标 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，传统的安全防护手段已难以满足需求，基于深度神经网络构建网络安全态势感知模型成为应对这一挑战的有效途径。本研究旨在建立一种高效准确的网络安全态势感知模型，以实现对网络环境中潜在威胁的实时监测与预警。为此，提出了一种融合多种深度学习算法的创新架构，该架构结合了卷积神经网络（CNN）用于特征提取、循环神经网络（RNN）处理时间序列数据以及自注意力机制优化信息传递路径。通过引入对抗训练机制提升模型鲁棒性，并采用半监督学习方法解决标注数据不足的问题。实验结果表明，在多个公开数据集上，所提模型相较于传统方法及现有先进模型，在检测率、误报率等关键性能指标方面均有显著提升，特别是在面对新型未知攻击时展现出更强的适应能力。本研究不仅为网络安全领域提供了新的技术思路，还推动了深度学习理论在实际应用中的进一步发展，对于构建更加智能可靠的网络防御体系具有重要意义。
关键词：网络安全态势感知；深度神经网络；卷积神经网络
Abstract
With the rapid development of information technology, cyber security threats have become increasingly complex and dynamic, rendering traditional security protection methods inadequate. To address this challenge, constructing a cyber security situational awareness model based on deep neural networks has emerged as an effective approach. This study aims to establish an efficient and accurate cyber security situational awareness model for real-time monitoring and early warning of potential threats in network environments. To achieve this, an innovative architecture integrating multiple deep learning algorithms is proposed, combining Convolutional Neural Networks (CNN) for feature extraction, Recurrent Neural Networks (RNN) for processing time-series data, and self-attention mechanisms to optimize information transmission paths. The robustness of the model is enhanced by incorporating adversarial training, while semi-supervised learning addresses the issue of insufficient labeled data. Experimental results demonstrate that, across multiple public datasets, the proposed model significantly outperforms traditional methods and existing advanced models in key performance metrics such as detection rate and false positive rate, particularly exhibiting stronger adaptability to novel and unknown attacks. This research not only provides new technical insights for the field of cyber security but also advances the practical application of deep learning theory, contributing significantly to the development of more intelligent and reliable network defense systems.
Keywords：Cybersecurity Situation Awareness; Deep Neural Network; 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、深度神经网络基础理论 2
(一) 深度学习基本原理 2
(二) 常用深度神经网络架构 3
(三) 网络安全中的应用挑战 4
三、数据采集与预处理技术 5
(一) 多源数据融合机制 5
(二) 异常流量特征提取 5
(三) 数据标注与质量控制 6
四、态势感知模型构建与优化 7
(一) 模型架构设计原则 7
(二) 关键算法选择依据 8
(三) 模型性能评估指标 9
结 论 10
参考文献 11
