Intelligent zero trust architecture for 5G/6G networks: Principles, challenges, and the role of machine learning in the context of O-RAN

被引:86
作者
Ramezanpour, Keyvan [1 ]
Jagannath, Jithin [1 ,2 ]
机构
[1] ANDRO Computat Solut LLC, Marconi Rosenblatt AI ML Innovat Lab, Rome, NY 13440 USA
[2] SUNY Buffalo, Dept Elect Engn, Buffalo, NY 14260 USA
关键词
Deep learning; 6G; 5G; Federated learning; Reinforcement learning; O-RAN; Zero-trust architecture; INTERNET;
D O I
10.1016/j.comnet.2022.109358
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
In this position paper, we discuss the critical need for integrating zero trust (ZT) principles into next-generation communication networks (5G/6G). We highlight the challenges and introduce the concept of an intelligent zero trust architecture (i-ZTA) as a security framework in 5G/6G networks with untrusted components. While network virtualization, software-defined networking (SDN), and service-based architectures (SBA) are key enablers of 5G networks, operating in an untrusted environment has also become a key feature of the networks. Further, seamless connectivity to a high volume of devices has broadened the attack surface on information infrastructure. Network assurance in a dynamic untrusted environment calls for revolutionary architectures beyond existing static security frameworks. To the best of our knowledge, this is the first position paper that presents the architectural concept design of an i-ZTA upon which modern artificial intelligence (AI) algorithms can be developed to provide information security in untrusted networks. We introduce key ZT principles as real-time Monitoring of the security state of network assets, Evaluating the risk of individual access requests, and Deciding on access authorization using a dynamic trust algorithm, called MED components. To ensure ease of integration, the envisioned architecture adopts an SBA-based design, similar to the 3GPP specification of 5G networks, by leveraging the open radio access network (O-RAN) architecture with appropriate real-time engines and network interfaces for collecting necessary machine learning data. Therefore, this work provides novel research directions to design machine learning based components that contribute towards i-ZTA for the future 5G/6G networks.
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页数:11
相关论文
共 43 条
[1]   A Survey on 5G Networks for the Internet of Things: Communication Technologies and Challenges [J].
Akpakwu, Godfrey Anuga ;
Silva, Bruno J. ;
Hancke, Gerhard P. ;
Abu-MAhfouz, Adnan M. .
IEEE ACCESS, 2018, 6 :3619-3647
[2]   Augmenting zero trust architecture to endpoints using blockchain: A state-of-the-art review [J].
Alevizos, Lampis ;
Ta, Vinh Thong ;
Hashem Eiza, Max .
SECURITY AND PRIVACY, 2022, 5 (01)
[3]  
[Anonymous], 2020, DoD Cloud Strategy
[4]   Zero Trust Architecture: Does It Help? [J].
Bertino, Elisa .
IEEE SECURITY & PRIVACY, 2021, 19 (05) :95-96
[5]   Never trust, always verify: A multivocal literature review on current knowledge and research gaps of zero-trust [J].
Buck, Christoph ;
Olenberger, Christian ;
Schweizer, Andre ;
Volter, Fabiane ;
Eymann, Torsten .
COMPUTERS & SECURITY, 2021, 110
[6]   Beyond Zero Trust: Trust Is a Vulnerability [J].
Campbell, Mark .
COMPUTER, 2020, 53 (10) :110-113
[7]   A Survey on Security Aspects for 3GPP 5G Networks [J].
Cao, Jin ;
Ma, Maode ;
Li, Hui ;
Ma, Ruhui ;
Sun, Yunqing ;
Yu, Pu ;
Xiong, Lihui .
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2020, 22 (01) :170-195
[8]  
Chandrashekar S, 2016, IEEE INT CONF COMM, P180, DOI 10.1109/ICCW.2016.7503785
[9]   A Security Awareness and Protection System for 5G Smart Healthcare Based on Zero-Trust Architecture [J].
Chen, Baozhan ;
Qiao, Siyuan ;
Zhao, Jie ;
Liu, Dongqing ;
Shi, Xiaobing ;
Lyu, Minzhao ;
Chen, Haotian ;
Lu, Huimin ;
Zhai, Yunkai .
IEEE INTERNET OF THINGS JOURNAL, 2021, 8 (13) :10248-10263
[10]  
Chen L., 2020, A survey of adversarial learning on graph, DOI [10.48550/arXiv.2003.05730, DOI 10.48550/ARXIV.2003.05730]