6G网络安全与隐私保护的研究现状及展望

摘要/Abstract

摘要： 5G网络部署的规模不断增长，虽然与4G相比优势明显，但是局限性也逐渐显现，这也促使针对6G网络技术开展研究.6G网络的复杂性和应用的多样性使得其安全问题更加突出，加上6G网络框架和相关技术很大程度上处于概念状态，其安全和隐私问题当前仍处于探索阶段.对6G安全和隐私研究现状进行分析，指出6G面临的安全挑战，从物理层安全、人工智能(AI)、分布式账本技术(distributed ledger technology, DLT)、边缘计算等方面讨论了6G的潜在安全解决方案，最后对未来研究趋势进行了展望.

关键词: 6G, 网络安全, 隐私保护, 人工智能安全, 信息安全

Abstract: The scale of 5G network deployments continues to grow. While there are obvious advantages over 4G network, the limitations of 5G network are emerging, which leads to research on 6G network technologies. The complexity of 6G network and the diversity of 6G’s applications make the security issues of 6G more prominent. Coupled with the fact that 6G frameworks and related technologies are largely in a conceptual state, the security and privacy issues of 6G network are still in the exploratory stage. In this paper we analyzed the current state of 6G security and privacy research at first, and than pointed out the security challenges in 6G network, discussed potential security solutions for 6G network from the aspects of physical layer security, artificial intelligence (AI), distributed ledger technology (DLT), and edge computing, and finally we provided an outlook on future research trends of security and privacy protection in 6G network.

Key words: 6G, network security, privacy protection, artificial intelligence security, information security

中图分类号:

TP309.2

李玲, 朱立东, 李卫榜, . 6G网络安全与隐私保护的研究现状及展望[J]. 信息安全研究, 2023, 9(9): 822-.

参考文献

［1］DeAlwis C, Kalla A, Pham Q V, et al. Survey on 6G frontiers: Trends, applications, requirements, technologies and future research［J］. IEEE Open Journal of the Communications Society, 2021, 2: 836886［2］Gui G, Liu M, Tang F, et al. 6G: Opening new horizons for integration of comfort, security, and intelligence［J］. IEEE Wireless Communications, 2020, 27(5): 126132［3］Ferrag M A, Maglaras L, Derhab A. Authentication and authorization for mobile IoT devices using biofeatures: Recent advances and future trends［J］. arXiv preprint, arXiv:1901.09374, 2019［4］Ji B, Han Y, Liu S, et al. Several key technologies for 6G: Challenges and opportunities［J］. IEEE Communications Standards Magazine, 2021, 5(2): 4451［5］Mucchi L, Jayousi S, Caputo S, et al. Physicallayer security in 6G networks［J］. IEEE Open Journal of the Communications Society, 2021, 2: 19011914［6］Marzetta T L. Noncooperative cellular wireless with unlimited numbers of base station antennas［J］. IEEE Trans on Wireless Communications, 2010, 9(11): 35903600［7］Bjrnson E, Sanguinetti L, Wymeersch H, et al. Massive MIMO is a reality—What is next?: Five promising research directions for antenna arrays［J］. Digital Signal Processing, 2019, 94: 320［8］Kapetanovic D, Zheng G, Rusek F. Physical layer security for massive MIMO: An overview on passive eavesdropping and active attacks［J］. IEEE Communications Magazine, 2015, 53(6): 2127 ［9］Timilsina S, Kudathanthirige D, Amarasuriya G. Physical layer security in cellfree massive MIMO［C］ Proc of IEEE Global Communications Conf (GLOBECOM). Piscataway, NI: IEEE, 2018: 17［10］Hoang T M, Ngo H Q, Duong T Q, et al. Cellfree massive MIMO networks: Optimal power control against activeeavesdropping［J］. IEEE Trans on Communications, 2018, 66(10): 47244737［11］Zhang X,Guo D, An K. Secure communication in multigroup multicasting cellfree massive mimo networks with active spoofing attack［J］. Electronics Letters, 2019, 55(2): 9698［12］Huang C, Zappone A, Alexandropoulos G C, et al. Reconfigurable intelligent surfaces for energy efficiency in wireless communication［J］. IEEE Trans on Wireless Communications, 2019, 18(8): 41574170［13］Cui M, Zhang G, Zhang R. Secure wireless communication via intelligent reflectingsurface［J］. IEEE Wireless Communications Letters, 2019, 8(5): 14101414［14］Zhang S, Zhang H, Di B, et al. Beyond intelligent reflecting surfaces: Reflectivetransmissive metasurface aided communications for fulldimensional coverage extension［J］. IEEE Trans on Vehicular Technology, 2020, 69(11): 1390513909［15］Shen S, Yu C, Zhang K, et al. Adaptive and dynamic security in AIempowered 6G: From an energy efficiency perspective［J］. IEEE Communications Standards Magazine, 2021, 5(3): 8088［16］Tariq F, Khandaker M R A, Wong K K, et al. A speculative study on 6G［J］. IEEE Wireless Communications, 2020, 27(4): 118125［17］Mao B, Kawamoto Y, Kato N. AIbased joint optimization of QoS and security for 6G energy harvesting Internet of things［J］. IEEE Internet of Things Journal, 2020, 7(8): 70327042［18］Xiao Y, Shi G, Krunz M. Towards ubiquitous AI in 6G with federated learning［J］. arXiv preprint, arXiv:2004.13563, 2020［19］Liu Y, Yuan X, Xiong Z, et al. Federated learning for 6G communications: Challenges, methods, and future directions［J］. China Communications, 2020, 17(9): 105118［20］Song Z, Sun H, Yang H H, et al. Reputationbased federated learning for secure wireless networks［J］. IEEE Internet of Things Journal, 2021, 9(2): 12121226［21］Zhao R, Yin Y, Shi Y, et al. Intelligent intrusion detection based on federated learning aided long shorttermmemory［J］. Physical Communication, 2020, 42: 101157［22］Zhang Z, Cao Y, Cui Z, et al. A manyobjective optimization based intelligent intrusion detection algorithm for enhancing security of vehicular networks in 6G［J］. IEEE Trans on Vehicular Technology, 2021, 70(6): 52345243［23］Lv Z, Qiao L, Li J, et al. Deeplearningenabled security issues in the Internet of things［J］. IEEE Internet of Things Journal, 2020, 8(12): 95319538［24］Zheng Z, Wang L, Zhu F, et al. Potential technologies and applications based on deep learning in the 6G networks［J］. Computers & Electrical Engineering, 2021, 95: 107373［25］Ankita A, Rani S. Machine learning and deep learning for malware and ransomware attacks in 6G network［C］ Proc of the 4th Int Conf on Computational Intelligence and Communication Technologies (CCICT). Piscataway, NJ: IEEE, 2021: 3944［26］Wang J, Ling X, Le Y, et al. Blockchainenabled wireless communications: A new paradigm towards 6G［J］. National Science Review, 2021, 8(9): nwab069［27］Sun W, Li S, Zhang Y. Edge caching inblockchain empowered 6G［J］. China Communications, 2021, 18(1): 117［28］Okon A A, Sholiyi O S, Elmirghani J M H, et al. Blockchain for spectrum management in 6G networks［M］ Wireless Blockchain: Principles, Technologies and Applications. Hoboken, NJ: John Wiley & Sons, 2021: 137159［29］Hewa T, Gür G, Kalla A, et al. The role of blockchain in 6G: Challenges, opportunities and research directions［C］ Proc of the 2nd 6G Wireless Summit (6G SUMMIT). Piscataway, NJ: IEEE, 2020: 15［30］Xu H, Klaine P V, Onireti O, et al. Blockchainenabled resource management and sharing for 6G communications［J］. Digital Communications and Networks, 2020, 6(3): 261269［31］Nguyen T, Tran N, Loven L, et al. Privacyaware blockchain innovation for 6G: Challenges and opportunities［C］ Proc of the 2nd 6G Wireless Summit (6G SUMMIT). Piscataway, NJ: IEEE, 2020: 15［32］Velliangiri S, Manoharan R, Ramachandran S, et al. Blockchain based privacy preserving framework for emerging 6G wireless communications［J］. IEEE Trans on Industrial Informatics, 2021, 18(7): 48684874［33］Liu L, Liang W, Mang G, et al. Blockchain based spectrum sharing over 6G hybrid cloud［C］ Proc of Int Wireless Communications and Mobile Computing (IWCMC). Piscataway, NJ: IEEE, 2021: 492497［34］Zhang H, Leng S, Wu F, et al. A DAG blockchain enhanced userautonomy spectrum sharing framework for 6Genabled IoT［J］. IEEE Internet of Things Journal, 2022, 9(11): 80128023［35］Aggarwal S, Kumar N, Tanwar S. Blockchainenvisioned UAV communication using 6G networks: Open issues, use cases, and future directions［J］. IEEE Internet of Things Journal, 2020, 8(7): 54165441［36］Patel F, Bhattacharya P, Tanwar S, et al. Block6Tel: Blockchainbased spectrum allocation scheme in 6Genvisioned communications［C］ Proc of Int Wireless Communications and Mobile Computing (IWCMC). Piscataway, NJ: IEEE, 2021: 18231828［37］Sun Z, Liang W, Qi F, et al.Blockchainbased dynamic spectrum sharing for 6G UIoT networks［J］. IEEE Network, 2021, 35(5): 143149［38］Mukherjee M, Kumar V, Guo M, et al. The interplay of reconfigurable intelligent surfaces and mobile edge computing in future wireless networks: A winwin strategy to 6G［J］. arXiv preprint, arXiv:2106.11784, 2021［39］Huynh L N T, Huh E N. Envisioning edge computing in future 6G wireless networks［C］ Proc of the 5th World Conf on Smart Trends in Systems Security and Sustainability (WorldS4). 2021: 307311［40］Bhat S A, Sofi I B, Chi C Y. Edge computing and its convergence with blockchain in 5G and beyond: Security, challenges, and opportunities［J］. IEEE Access, 2020, 8: 205340205373［41］Ishtiaq M, Saeed N, Khan M A. Edge computing in IoT: A 6G perspective［J］. arXiv preprint, arXiv:2111.08943, 2021［42］Mathew A. Edgecomputing and its convergence with blockchain in 6G: Security challenges［J］. International Journal of Computer Science and Mobile Computing, 2021, 10(8): 814［43］Prathiba S B, Raja G, Anbalagan S, et al. Cybertwindriven federated learning based personalized service provision for 6GV2X［J］. IEEE Trans on Vehicular Technology, 2021, 71(5): 46324641［44］Hui Y, Cheng N, Su Z, et al. Secure and personalized edge computing services in 6G heterogeneous vehicular networks［J］. IEEE Internet of Things Journal, 2021, 9(8): 59205931［45］Hu J, Chen C, Cai L, et al. UAVassisted vehicular edge computing for the 6G Internet of vehicles: Architecture, intelligence, and challenges［J］. IEEE Communications Standards Magazine, 2021, 5(2): 1218［46］AlAnsi A, AlAnsi A M, Muthanna A, et al. Survey on intelligence edge computing in 6G: Characteristics, challenges, potential use cases, and market drivers［J］. Future Internet, 2021, 13(5): 123［47］Ahmad I, Lembo S, Rodriguez F, et al. Security of micro MEC in 6G: A brief overview［C］ Proc of the 19th IEEE Annual Consumer Communications & Networking Conf (CCNC). Piscatawy, NJ: IEEE, 2022: 332337 ［48］Huynh L N T, Huh E N. Envisioning edge computing in future 6G wireless networks［C］ Proc of the 5th World Conf on Smart Trends in Systems Security and Sustainability (WorldS4). Piscatawy, NJ: IEEE, 2021: 307311［49］Fadlullah Z M, Kato N. HCP: Heterogeneous computing platform for federated learning based collaborative content caching towards 6G networks［J］. IEEE Trans on Emerging Topics in Computing, 2020, 10(1): 112123［50］Wu X, Zhang Y, Shi M, et al. An adaptive federated learning scheme with differential privacypreserving［J］. Future Generation Computer Systems, 2022, 127: 362372［51］Yang M, Wang X, Qian H, et al. An improved federated learning algorithm for privacypreserving in cybertwindriven 6G system［J］. IEEE Trans on Industrial Informatics, 2022, 18(10): 67336742 ［52］Wang Y, Su Z, Zhang N, et al. Learning in the air: Secure federated learning for UAVassistedcrowdsensing［J］. IEEE Trans on Network Science and Engineering, 2020, 8(2): 10551069［53］Ferrag M A, Friha O, Maglaras L, et al. Federated deep learning for cyber security in the Internet of things: Concepts, applications, and experimental analysis［J］. IEEE Access, 2021, 9: 138509138542［54］陈烨, 王永利. 基于区块链的5G隐私保护模型研究［J］. 信息安全研究, 2022, 8(1): 4348［55］仲蓓鑫, 林浩, 孔苏鹏, 等. 基于区块链技术的多源网络数据隐私保护方法［J］. 信息安全研究, 2021, 7(1): 8689［56］Zhou X, Liang W, She J, et al. Twolayer federated learning with heterogeneous model aggregation for 6G supported Internet of vehicles［J］. IEEE Trans on Vehicular Technology, 2021, 70(6): 53085317［57］Li Q, Lin X. Efficient andprivacypreserving speaker recognition for cybertwindriven 6G［J］. IEEE Internet of Things Journal, 2021, 8(22): 1619516206［58］Sun Y, Liu J, Wang J, et al. When machine learning meets privacy in 6G: A survey［J］. IEEE Communications Surveys & Tutorials, 2020, 22(4): 26942724［59］Ylianttila M, Kantola R, Gurtov A, et al. 6G white paper: Research challenges for trust, security and privacy［J］. arXiv preprint, arXiv:2004.11665, 2020［60］Porambage P, Gür G, Osorio D P M, et al. 6G security challenges and potential solutions［C］ Proc of Joint European Conf on Networks and Communications & 6G Summit (EuCNC6G Summit). Piscataway, NJ: IEEE, 2021: 622627［61］Porambage P, Gür G, Osorio D P M, et al. The roadmap to 6G security and privacy［J］. IEEE Open Journal of the Communications Society, 2021, 2: 10941122［62］Sattiraju R, Weinand A, Schotten H D. Performance analysis of deep learning based on recurrent neural networks for channel coding［C］ Proc of IEEE Int Conf on Advanced Networks and Telecommunications Systems (ANTS). Piscataway, NJ: IEEE, 2018: 16［63］Weinand A, Karrenbauer M, Lianghai J, et al. Physical layer authentication for mission critical machine type communication using Gaussian mixture model based clustering［J］. arXiv prepring, arXiv:1711.06101, 2017［64］Kang J, Xiong Z, Niyato D, et al. Incentive mechanism for reliable federated learning: A joint optimization approach to combining reputation and contract theory［J］. IEEE Internet of Things Journal, 2019, 6(6): 1070010714

[1]	王耀辉, 王可, 宫良一, 付豫豪, 王跃达, 李婧, . 基于异构图的恶意域名检测方法研究[J]. 信息安全研究, 2023, 9(E1): 38-.
[2]	李娇, 张玉清, 吴亚飚, . 网络安全领域小样本命名实体识别方法[J]. 信息安全研究, 2023, 9(E1): 51-.
[3]	李雅硕, 龙春, 魏金侠, 李婧, 杨帆, 李婧, . 基于同态加密的人脸识别隐私保护方法[J]. 信息安全研究, 2023, 9(9): 843-.
[4]	滕亮, 陈兵, 赵开斌, 徐涛, 李京昆, 洪薇, . 基于区块链的医疗数据安全共享模型研究与应用[J]. 信息安全研究, 2023, 9(9): 884-.
[5]	盛丹丹, . 基于大数据分析的隐私信息保护系统设计与实现[J]. 信息安全研究, 2023, 9(9): 914-.
[6]	马龙, 张乐, 寇猛, 董睿. 基于多方安全攻防博弈的民航旅客隐私数据保护模型[J]. 信息安全研究, 2023, 9(8): 799-.
[7]	杜林, 许传淇. 基于BERT的漏洞文本特征分类技术研究[J]. 信息安全研究, 2023, 9(7): 687-.
[8]	杨代才. 气象大数据云平台的网络安全防护体系设计[J]. 信息安全研究, 2023, 9(7): 701-.
[9]	朱孟垚, 李兴华. ChatGPT安全威胁研究[J]. 信息安全研究, 2023, 9(6): 533-.
[10]	张昊星, 赵景欣, 岳星辉, 任家东, . 全生命周期数据安全管理和人工智能技术的融合研究[J]. 信息安全研究, 2023, 9(6): 543-.
[11]	高亚楠, . 大模型技术的网络安全治理和应对研究[J]. 信息安全研究, 2023, 9(6): 551-.
[12]	李宗维, 孔德潮, 牛媛争, 彭红利, 李晓琦, 李文凯, . 基于人工智能和区块链融合的隐私保护技术研究综述[J]. 信息安全研究, 2023, 9(6): 557-.
[13]	屈梦楠, 靳宇浩, 邬江. 基于隐式对称生成对抗网络的图像隐写与提取方案[J]. 信息安全研究, 2023, 9(6): 566-.
[14]	曹婉莹, 曹旭栋, 葛平原, 张玉清, . 中美网络安全漏洞披露与共享政策研究[J]. 信息安全研究, 2023, 9(6): 602-.
[15]	王涛, 谭虎, 徐亭亭, 辛保江, 刘刚, 周潘, . 基于迭代二分聚类的K-匿名机制[J]. 信息安全研究, 2023, 9(5): 402-.