The development of the telecommunication networks observed in present and future time is impressive. Today we witness rapid implementation of 5G networks. We can say that this actually is the moment when (artificial intelligence) AI enters at small door but in the beyond 5G world it is expected to have the prime role in smart operation, management and maintenance of non-software defined networking (SDN), network function virtualization (NFV) and especially at SDN and NFV aware networks. Number of standardization body’s and work groups are focused in a way to create a framework that will define the future use of AI and security standards necessary to exist in order to create health environment for the next generation telecommunication infrastructure. In the wireless world AI/Machine learning (ML) has great potential to shake the way we operate and to become foundation of the transformation that leads to the next industrial revolution. Network virtualization gives flexibility and freedom of the telco operators to choose the hardware and network topology they need for AI/ML platforms and big data sets. 5G and IoT create positive environment for AI and ML development and usage. As the network requirements are developed and the number of the users raises, gains are expected to grow with the number of variables and the interactions among them so it becomes impossible to relay on humans to control the network for increased number of variables and this is why AI with ML and automation become beneficial and necessity to run the future networks. AI generally is defined as capacity of mind or ability to acquire and apply knowledge and skills while ML is defined as learning that does not require explicit programming. Combined usage of AI and ML can optimize almost any component of the wireless network, this does not mean that it should be used everywhere mainly because at the end of the day the cost benefit analysis of its usage must be positive. Smart operation, management and infrastructure maintenance (SOMM) networks are defined as: Intelligent, data driven, integrated and agile. Today AI is introduced but in future it will represent the network engine. It is interesting to mention that network security must be upgraded because the network will provide services for massive number of IoT devices that will have variety of functions and requests. AI/ML can improve the security services and to be used in order to elevate them at advanced level. In this text we focus our attention at AI/ML and security scenarios defined for IoT in 5G environment.
Recommendation ITU-T M.3041 “Framework of smart operation, management and maintenance” – Series M: Telecommunication management including TMN and network maintenance – Telecommunications management network - 02.2020.
Recommendation ITU-T Y.3172 “Architectural framework for machine learning in future networks including IMT-2020” – Series Y: Global information infrastructure, internet protocol aspects, next generation networks, internet of things and smart cities, Future networks-06.2019.
Recommendation ITU-T Y.2701 “Security requirements for NGN release 1” Series Y: Global information infrastructure, internet protocol aspects and next generation networks, Next Generation Networks- Security.
Recommendation ITU-T Y.3101 “Requirements of the IMT-2000 network” - Series Y: Global information infrastructure, internet protocol aspects, next generation networks, internet of things and smart cities, Future networks-01.2018.
Recommendation ITU-T Y.3173 “Framework for evaluating intelligence levels of future networks including IMT-2020” - Series Y: Global information infrastructure, internet protocol aspects, next generation networks, internet of things and smart cities, Future networks-02.2020
Recommendation ITU-T Y.3174 “Framework for evaluating intelligence levels of future networks including IMT-2020” - Series Y: Global information infrastructure, internet protocol aspects, next generation networks, internet of things and smart cities, Future networks-02.2020.
ITU-T Y-series Recommendations – Supplement 59 “ITU-T Y.3100-series – IMT-2020 standardization roadmap” - Series Y: Global information infrastructure, internet protocol aspects, next generation networks, internet of things and smart cities, Future networks-03.2020.
ITU report on AI and IoT in Security Aspects “Artificial Intelligence (AI) for Development series” 07.2018
Recommendation ITU-T Y.3053 “Framework of trustworthy networking with trust-centric network domains” - Series Y: Global information infrastructure, internet protocol aspects, next generation networks, internet of things and smart cities, Future networks-01.2018
C. Jiang, H. Zhang, Y. Ren, Z. Han, K. C. Chen, and L. Hanzo, “Machine Learning Paradigms for Next-Generation Wireless Networks,” IEEE Wireless Commun. Mag., vol. 24, no. 2, 2018, pp. 98-105.
H. Fang, X. Wang, and L. Hanzo, “Learning-aided Physical Layer Authentication as an Intelligent Process,” IEEE Trans. Commun., vol. 67, no. 3, 2019, pp. 2260-2273.
Sang-Hyun Park, “D3.1 - Intermediate Report On Enhanced 5g Radio Access Technologies,” Ref. Ares (2019)6421524 - 17/10/2019.
W. Hou, X. Wang, J. Chouinard, and A. Refaey, “Physical Layer Authentication for Mobile Systems with Time-Varying Carrier Frequency Offsets,” IEEE Trans. Commun., vol. 62, no. 5, 2014, pp. 1658-1667.
W. Wang, Z. Sun, S. Piao, B. Zhu, and K. Ren, “Wireless Physical Layer Identification: Modeling and Validation,” IEEE Trans. Inf. Forensics Security, vol. 11, no. 9, 2016, pp. 2091-2109.
Y. Liu, H. H. Chen, and L. Wang, “Physical Layer Security for Next Generation Wireless Networks: Theories, Technologies, and Challenges,” IEEE Commun. Surveys Tuts., vol. 19, no. 1, 2017, pp. 347-376.
S. Tomasin, “Analysis of Channel-based User Authentication by Key-less and Key-based approaches,” IEEE Trans.Wireless Commun., vol. 17, no. 9, 2018, pp. 5700-5712.
L. Xiao, X. Wan, X. Lu, Y. Zhang, and D. Wu, “IoT Security Techniques based on Machine Learning: How do IoT Devices use AI to Enhance Security?” IEEE Signal Process. Mag., vol. 35, no. 5, 2018, pp. 41-49.
F. Restuccia, S. D’Oro, and T. Melodia, “Securing the Internet of Things in the Age of Machine Learning and Software-Defined Networking,” IEEE Internet Things J., vol. 5, no. 6, 2018, pp. 4829-4842.
U. Challita, A. Ferdowsi, M. Chen, and W. Saad, “Machine Learning for Wireless Connectivity and Security of Cellular-Connected.
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