| 69 | 0 | 3 |
| 下载次数 | 被引频次 | 阅读次数 |
针对6G网络架构中的需求与挑战，探讨了“主动免疫-孪生互驱-弹性自治-分布协同”的智慧内生安全架构。该架构通过预训练威胁表征模型实现攻击前预判，利用数字孪生构建虚实结合防御体系，借助联邦学习与区块链技术建立跨域协同机制，形成“感知-决策-验证-优化”的安全闭环。介绍了5项6G网络安全中的关键技术：分布式机器学习、AI大模型、轻量级认证授权与访问控制、数字孪生、无线物理层安全技术，为6G网络的高效可信运行提供理论支撑与技术路径分析。
Abstract：In response to the requirements and challenges of the 6G network architecture, an intelligent endogenous security architecture of “proactive immunity, twin-driven, resilient autonomy, and distributed collaboration” is proposed. This architecture enables proactive threat prediction through pre-trained threat representation models, constructs a cyber-physical integrated defense system using digital twins, and leverages federated learning and blockchain technologies to facilitate cross-domain collaboration, forming a security closed loop of “perception, decision, verification, and optimization.” Five key technologies for 6G network security are introduced: distributed machine learning, AI large models, lightweight authentication and access control, digital twins, and wireless physical layer security technologies. These technologies provide theoretical support and technical pathways for the efficient and trustworthy operation of 6G networks.
[1]张成磊,付玉龙,李晖,等. 6G网络安全场景分析及安全模型研究[J].网络与信息安全学报, 2021, 7(1):28-45
[2] CAVIGLIONE L, GAGGERO M, LALANDE J F, et al. Seeing the unseen:revealing mobile malware hidden communications via energy consumption and artificial intelligence[J]. IEEE transactions on information forensics and security, 2016, 11(4):799-810. DOI:10.1109/TIFS.2015.2510825
[3] BOTHA M, SOLMS R, PERRY K, et al. The utilization of artificial intelligence in a hybrid intrusion detection system[EB/OL].[2025-03-20]. https://dl.acm.org/doi/10.5555/581506.581527
[4]蔡良伟,程璐,李军,等.基于遗传算法的正则表达式规则分组优化[J].深圳大学学报理工版, 2015, 32(3):281-289
[5]粟栗,庄小君,杜海涛,等. 6G网络内生安全架构研究[EB/OL].(2022-01-20)[2025-03-18]. http://scis. scichina. com/cn/2022/SSI-2021-0257.pdf
[6]张秀贤,朱晓荣. 6G网络中分布式AI模型协同技术综述[J].移动通信, 2025, 49(1):43-51
[7]吴官翰,贾维敏,赵建伟,等.基于多智能体强化学习的混合博弈模式下多无人机辅助通信系统设计[J].电子与信息学报, 2022, 44(3):940-950. DOI:10.11999/JEIT210662
[8]王志勤,江甲沫,刘沛西,等. 6G联邦边缘学习新范式:基于任务导向的资源管理策略[J].通信学报, 2022, 43(6):16-27. DOI:10.11959/j.issn.1000-436x.2022128
[9]秦鹏达.大模型让安全防护升级跃迁:如何让安全更加智能化[EB/OL].(2024-04-03)[2025-02-01]. https://mp. weixin. qq. com/s/GCijFpmdc0VbH--R1tDsdA
[10]田辉,倪万里,聂高峰,等. 6G网络中面向AI大模型的联邦学习与协同部署技术综述[J].移动通信, 2024, 48(8):30-40
[11] CHAUDHRY S A, IRSHAD A, KHAN M A, et al. A lightweight authentication scheme for 6G-IoT enabled maritime transport system[J]. IEEE transactions on inteligent transportation systems,2023, 24(2):2401-2410. DOI:10.1109/tits.2021.3134643
[12] OMETOV A, BEZZATEEV S, M?KITALO N, et al. Multi-factor authentication:a survey[J]. Cryptography, 2018, 2(1):1. DOI:10.3390/cryptography2010001
[13]刘光毅,邓娟,郑青碧.基于数字孪生网络的6G无线网络自治[J].中兴通讯技术, 2023, 29(3):2-7
[14]戴峤,宋华伟,金梁,等.基于等效信道的物理层认证和密钥分发机制[J].中国科学:信息科学, 2014, 44(12):1580-1592. DOI:10.1360/N112013-00041
[15]朱政宇,王梓晅,徐金雷,等.智能反射面辅助的未来无线通信:现状与展望[J].航空学报, 2022, 43(2):203-217. DOI:10.7527/S1000-6893.2021.25014
[16]陈智,刘轲,李玲香,等.太赫兹通信感知一体化技术综述[J].中国科学:信息科学, 2024, 54(5):1215-1235. DOI:10.1360/SSI-2023-0354
[17]施剑阳,牛文清,徐增熠,等.面向6G的可见光通信关键技术[J].无线电通信技术, 2021, 47(6):692-697. DOI:10.3969/j.issn.1003-3114.2021.06.003
基本信息:
DOI：
中图分类号:TN929.5;TP18
引用信息:
[1]王瀚洲,金子安,王瑞等.AI赋能6G网络安全：架构与关键技术[J].中兴通讯技术,2025,31(03):31-38.
基金信息:
国家重点研发计划项目（2021YFB2700200）; 国家自然科学基金项目（U21B2021,61972018,61932014）