摘 要
随着5G网络的快速发展与广泛应用，其高带宽、低时延和海量连接特性为各行各业带来前所未有的机遇，同时也带来了更为复杂的安全挑战。传统安全检测手段难以满足5G网络环境下实时性、精准性的要求，为此构建面向5G网络的实时安全检测与预警机制具有重要意义。本研究旨在针对5G网络架构特点及业务需求，提出一种融合多源异构数据采集、深度学习算法分析以及智能决策反馈于一体的实时安全检测与预警机制。通过在5G网络边缘侧部署轻量级数据采集模块，获取包括信令流量、用户行为等在内的多维度数据；利用深度神经网络模型对采集到的数据进行特征提取与异常识别，实现对潜在威胁的快速感知；结合专家系统制定合理的响应策略并及时发出预警信息。实验结果表明该机制能够有效提升5G网络安全态势感知能力，在保证较低误报率的同时大幅缩短了从威胁出现到采取措施的时间间隔，为保障5G网络稳定运行提供了可靠的技术支撑，创新性地解决了5G网络环境下安全检测面临的实时性和准确性难题，为后续相关研究奠定了坚实基础。
关键词：5G网络安全；实时安全检测；深度学习
Abstract
With the rapid development and widespread application of 5G networks, their characteristics of high bandwidth, low latency, and massive connectivity have brought unprecedented opportunities to various industries, while also presenting more complex security challenges. Traditional security detection methods struggle to meet the requirements of real-time and accuracy in a 5G network environment, making it crucial to establish a real-time security detection and early warning mechanism tailored for 5G networks. This study proposes an integrated real-time security detection and early warning mechanism that combines multi-source heterogeneous data collection, deep learning algorithm analysis, and intelligent decision-making feedback, specifically addressing the architectural features and service requirements of 5G networks. By deploying lightweight data acquisition modules at the edge of 5G networks, multidimensional data including signaling traffic and user behavior are collected; deep neural network models are utilized for feature extraction and anomaly detection from the collected data, enabling rapid perception of potential threats. Coupled with an expert system to formulate appropriate response strategies and issue timely warnings, this mechanism has been shown through experimental results to effectively enhance the cybersecurity situational awareness of 5G networks. It significantly reduces the time interval from threat emergence to action while maintaining a low false alarm rate, providing reliable technical support for ensuring the stable operation of 5G networks. This approach innovatively addresses the challenges of real-time and accurate security detection in 5G network environments, laying a solid foundation for subsequent related research.
Keywords：5G Network Security；Real-Time Security Detection；Deep Learning
目 录
引 言 1
第一章 5G网络安全需求分析 2
1.1 实时性要求对安全的影响 2
1.2 安全检测的关键需求 2
第二章 实时安全检测技术研究 4
2.1 检测技术现状综述 4
2.2 基于AI实时检测 4
2.3 多源数据融合检测机制 5
第三章 预警机制设计与实现 7
3.1 预警模型构建原则 7
3.2 实时威胁评估算法 7
3.3 预警信息传播策略 8
第四章 系统集成与应用案例 10
4.1 系统架构设计要点 10
4.2 关键技术集成方案 10
4.3 典型应用场景分析 11
结 论 13
参考文献 14
致 谢 15
随着5G网络的快速发展与广泛应用，其高带宽、低时延和海量连接特性为各行各业带来前所未有的机遇，同时也带来了更为复杂的安全挑战。传统安全检测手段难以满足5G网络环境下实时性、精准性的要求，为此构建面向5G网络的实时安全检测与预警机制具有重要意义。本研究旨在针对5G网络架构特点及业务需求，提出一种融合多源异构数据采集、深度学习算法分析以及智能决策反馈于一体的实时安全检测与预警机制。通过在5G网络边缘侧部署轻量级数据采集模块，获取包括信令流量、用户行为等在内的多维度数据；利用深度神经网络模型对采集到的数据进行特征提取与异常识别，实现对潜在威胁的快速感知；结合专家系统制定合理的响应策略并及时发出预警信息。实验结果表明该机制能够有效提升5G网络安全态势感知能力，在保证较低误报率的同时大幅缩短了从威胁出现到采取措施的时间间隔，为保障5G网络稳定运行提供了可靠的技术支撑，创新性地解决了5G网络环境下安全检测面临的实时性和准确性难题，为后续相关研究奠定了坚实基础。
关键词：5G网络安全；实时安全检测；深度学习
Abstract
With the rapid development and widespread application of 5G networks, their characteristics of high bandwidth, low latency, and massive connectivity have brought unprecedented opportunities to various industries, while also presenting more complex security challenges. Traditional security detection methods struggle to meet the requirements of real-time and accuracy in a 5G network environment, making it crucial to establish a real-time security detection and early warning mechanism tailored for 5G networks. This study proposes an integrated real-time security detection and early warning mechanism that combines multi-source heterogeneous data collection, deep learning algorithm analysis, and intelligent decision-making feedback, specifically addressing the architectural features and service requirements of 5G networks. By deploying lightweight data acquisition modules at the edge of 5G networks, multidimensional data including signaling traffic and user behavior are collected; deep neural network models are utilized for feature extraction and anomaly detection from the collected data, enabling rapid perception of potential threats. Coupled with an expert system to formulate appropriate response strategies and issue timely warnings, this mechanism has been shown through experimental results to effectively enhance the cybersecurity situational awareness of 5G networks. It significantly reduces the time interval from threat emergence to action while maintaining a low false alarm rate, providing reliable technical support for ensuring the stable operation of 5G networks. This approach innovatively addresses the challenges of real-time and accurate security detection in 5G network environments, laying a solid foundation for subsequent related research.
Keywords：5G Network Security；Real-Time Security Detection；Deep Learning
目 录
引 言 1
第一章 5G网络安全需求分析 2
1.1 实时性要求对安全的影响 2
1.2 安全检测的关键需求 2
第二章 实时安全检测技术研究 4
2.1 检测技术现状综述 4
2.2 基于AI实时检测 4
2.3 多源数据融合检测机制 5
第三章 预警机制设计与实现 7
3.1 预警模型构建原则 7
3.2 实时威胁评估算法 7
3.3 预警信息传播策略 8
第四章 系统集成与应用案例 10
4.1 系统架构设计要点 10
4.2 关键技术集成方案 10
4.3 典型应用场景分析 11
结 论 13
参考文献 14
致 谢 15
