Skip to main content

Network Security and Trustworthiness

  • Chapter
  • First Online:
Fundamentals of 6G Communications and Networking

Part of the book series: Signals and Communication Technology ((SCT))

  • 774 Accesses

Abstract

In 6G, a range of new technologies will emerge based on intelligent network structures. The connection between 6G technology and AI-based intelligent networks will make communication more convenient for humans, but may also rise to new security threats. As a result, network security and trustworthiness will become a key challenges in 6G. This chapter categorizes security threats in 6G into four areas; openness, post quantum cryptography, privacy preserving, and auction threats. We will also introduce security solutions that can mitigate these security threats in 6G.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. V. Ziegler, P. Schneider, H. Viswanathan, M. Montag, S. Kanugovi, and A. Rezaki, “Security and Trust in the 6G Era,” IEEE Access, vol. 9, pp. 142 314–142 327, 2021.

    Google Scholar 

  2. C. De Alwis, A. Kalla, Q. V. Pham, P. Kumar, K. Dev, W. J. Hwang, and M. Liyanage, “Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research,” IEEE Open Journal of the Communications Society, vol. 2, pp. 836–886, 2021.

    Article  Google Scholar 

  3. X. You, C.-X. Wang, J. Huang, X. Gao, Z. Zhang, M. Wang, Y. Huang, C. Zhang, Y. Jiang, J. Wang et al., “Towards 6G Wireless Communication Networks: Vision, Enabling Technologies, and New Paradigm Shifts,” Science China Information Sciences, vol. 64, no. 1, pp. 1–74, 2021.

    Article  Google Scholar 

  4. P. Porambage, G. Gür, D. P. M. Osorio, M. Liyanage, A. Gurtov, and M. Ylianttila, “The Roadmap to 6G Security and Privacy,” IEEE Open Journal of the Communications Society, vol. 2, pp. 1094–1122, 2021.

    Article  Google Scholar 

  5. D. H. Je, J. Jung, and S. Choi, “Toward 6G Security: Technology Trends, Threats, and Solutions,” IEEE Communications Standards Magazine, vol. 5, no. 3, pp. 64–71, 2021.

    Article  Google Scholar 

  6. V. Lenarduzzi, D. Taibi, D. Tosi, L. Lavazza, and S. Morasca, “Open Source Software Evaluation, Selection, and Adoption: a Systematic Literature Review,” in Proc. of Euromicro Conference on Software Engineering and Advanced Applications (SEAA). Portoroz, Slovenia: IEEE, August 2020, pp. 437–444.

    Google Scholar 

  7. V.-L. Nguyen, P.-C. Lin, B.-C. Cheng, R.-H. Hwang, and Y.-D. Lin, “Security and Privacy for 6G: A Survey on Prospective Technologies and Challenges,” IEEE Communications Surveys & Tutorials, vol. 23, no. 4, pp. 2384–2428, 2021.

    Article  Google Scholar 

  8. A. V. Mota, S. Azam, B. Shanmugam, K. C. Yeo, and K. Kannoorpatti, “Comparative Analysis of Different Techniques of Encryption for Secured Data Transmission,” in Proc. of IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI). Chennai, India: IEEE, September 2017, pp. 231–237.

    Google Scholar 

  9. P. Regulation, “General Data Protection Regulation,” Proc. of Intouch, vol. 25, pp. 1–8, 2018.

    Google Scholar 

  10. P. Pleva, “A Revised Classification of Anonymity,” CoRR, vol. abs/1211.5613, 2012.

    Google Scholar 

  11. J. Davidson, B. Liebald, J. Liu, P. Nandy, T. Van Vleet, U. Gargi, S. Gupta, Y. He, M. Lambert, B. Livingston et al., “The Youtube Video Recommendation System,” in Proc. of the ACM conference on Recommender systems, Barcelona, Spain, September 2010, pp. 293–296.

    Google Scholar 

  12. Y. Siriwardhana, P. Porambage, M. Liyanage, and M. Ylianttila, “AI and 6G Security: Opportunities and Challenges,” in Proc. of Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit). Porto, Portugal: IEEE, June 2021, pp. 616–621.

    Google Scholar 

  13. Y. Yang, L. Wu, G. Yin, L. Li, and H. Zhao, “A Survey on Security and Privacy Issues in Internet-of-Things,” IEEE Internet of Things Journal, vol. 4, no. 5, pp. 1250–1258, 2017.

    Article  Google Scholar 

  14. V. Krishna, Auction Theory. Academic Press, September 2009.

    Google Scholar 

  15. P. Klemperer, “Auction Theory: A Guide to the Literature,” Journal of Economic Surveys, vol. 13, no. 3, pp. 227–286, December 1999.

    Article  Google Scholar 

  16. E. Maskin et al., Auctions and Efficiency. School of Social Science, Institute for Advanced Study, 2001.

    Google Scholar 

  17. M. Shin, J. Kim, and M. Levorato, “Auction-Based Charging Scheduling with Deep Learning Framework for Multi-Drone Networks,” IEEE Transactions on Vehicular Technology, vol. 68, no. 5, pp. 4235–4248, May 2019.

    Article  Google Scholar 

  18. X. X. Wang and W. Wu, “Towards Truthful Auction Mechanisms for Task Assignment in Mobile Device Clouds,” in IEEE conference on Computer Communications (INFOCOM), Atlanta, GA, USA, May 2017, pp. 1–9.

    Google Scholar 

  19. S. A. Abdel Hakeem, H. H. Hussein, and H. Kim, “Security Requirements and Challenges of 6G Technologies and Applications,” Sensors, vol. 22, no. 5, p. 1969, 2022.

    Google Scholar 

  20. Y. Qi, G. Yang, L. Liu, J. Fan, A. Orlandi, H. Kong, W. Yu, and Z. Yang, “5G Over-the-Air Measurement Challenges: Overview,” IEEE Transactions on Electromagnetic Compatibility, vol. 59, no. 6, pp. 1661–1670, 2017.

    Article  Google Scholar 

  21. M. M. Villegas, C. Orellana, and H. Astudillo, “A Study of Over-the-Air (OTA) Update Systems for CPS and IoT Operating Systems,” in Proc. of European Conference on Software Architecture ECSA, vol. 2, Paris, France, September 2019, pp. 269–272.

    Google Scholar 

  22. X. Chen, W. Feng, N. Ge, and Y. Zhang, “Zero Trust Architecture for 6G Security,” CoRR, vol. abs/2203.07716, 2022.

    Google Scholar 

  23. M. Campbell, “Beyond Zero Trust: Trust Is a Vulnerability,” Computer, vol. 53, no. 10, pp. 110–113, 2020.

    Article  Google Scholar 

  24. S. Rose, O. Borchert, S. Mitchell, and S. Connelly, “Zero Trust Architecture,” National Institute of Standards and Technology, Tech. Rep., 2020.

    Google Scholar 

  25. J. Ahn, H.-Y. Kwon, B. Ahn, K. Park, T. Kim, M.-K. Lee, J. Kim, and J. Chung, “Toward Quantum Secured Distributed Energy Resources: Adoption of Post-Quantum Cryptography (PQC) and Quantum Key Distribution (QKD),” Energies, vol. 15, no. 3, p. 714, 2022.

    Google Scholar 

  26. P. W. Shor, “Algorithms for Quantum Computation: Discrete Logarithms and Factoring,” in Proc. of Annual Symposium on Foundations of Computer Science. New Mexico, USA: IEEE, November 1994, pp. 124–134.

    Chapter  Google Scholar 

  27. V. Scarani, H. Bechmann-Pasquinucci, N. J. Cerf, M. Dušek, N. Lütkenhaus, and M. Peev, “The Security of Practical Quantum Key Distribution,” Reviews of modern physics, vol. 81, no. 3, p. 1301, 2009.

    Google Scholar 

  28. Y. Kwak, W. J. Yun, J. P. Kim, H. Cho, M. Choi, S. Jung, and J. Kim, “Quantum Heterogeneous Distributed Deep Learning Architectures: Models, Discussions, and Applications,” CoRR, vol. abs/2202.11200, 2022.

    Google Scholar 

  29. W. Zhang, D.-S. Ding, Y.-B. Sheng, L. Zhou, B.-S. Shi, and G.-C. Guo, “Quantum Secure Direct Communication with Quantum Memory,” Physical review letters, vol. 118, no. 22, p. 220501, 2017.

    Google Scholar 

  30. G.-L. Long and X.-S. Liu, “Theoretically Efficient High-Capacity Quantum-Key-Distribution Scheme,” Physical Review A, vol. 65, no. 3, p. 032302, 2002.

    Google Scholar 

  31. W. Li, S. Lu, and D.-L. Deng, “Quantum Federated Learning Through Blind Quantum Computing,” Science China Physics, Mechanics & Astronomy, vol. 64, no. 10, pp. 1–8, 2021.

    Article  Google Scholar 

  32. S. Barz, E. Kashefi, A. Broadbent, J. F. Fitzsimons, A. Zeilinger, and P. Walther, “Demonstration of Blind Quantum Computing,” science, vol. 335, no. 6066, pp. 303–308, 2012.

    Article  MathSciNet  MATH  Google Scholar 

  33. A. Acar, H. Aksu, A. S. Uluagac, and M. Conti, “A Survey on Homomorphic Encryption Schemes: Theory and Implementation,” ACM Computing Surveys (Csur), vol. 51, no. 4, pp. 1–35, 2018.

    Article  Google Scholar 

  34. J. Li, X. Kuang, S. Lin, X. Ma, and Y. Tang, “Privacy Preservation for Machine Learning Training and Classification Based on Homomorphic Encryption Schemes,” Information Sciences, vol. 526, pp. 166–179, 2020.

    Article  MathSciNet  MATH  Google Scholar 

  35. J. Konečnỳ, H. B. McMahan, D. Ramage, and P. Richtárik, “Federated Optimization: Distributed Machine Learning for On-Device Intelligence,” CoRR, vol. abs/1610.02527, 2016.

    Google Scholar 

  36. Y. Matsubara, D. Callegaro, S. Baidya, M. Levorato, and S. Singh, “Head network distillation: Splitting distilled deep neural networks for resource-constrained edge computing systems,” IEEE Access, vol. 8, pp. 212 177–212 193, 2020.

    Google Scholar 

  37. M. Shin, C. Hwang, J. Kim, J. Park, M. Bennis, and S.-L. Kim, “XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning,” CoRR, vol. abs/2006.05148, 2020.

    Google Scholar 

  38. N. Truong, K. Sun, S. Wang, F. Guitton, and Y. Guo, “Privacy Preservation in Federated Learning: An Insightful Survey from the GDPR Perspective,” Computers & Security, vol. 110, p. 102402, 2021.

    Article  Google Scholar 

  39. Y. Sun, J. Liu, J. Wang, Y. Cao, and N. Kato, “When Machine Learning Meets Privacy in 6G: A Survey,” IEEE Communications Surveys & Tutorials, vol. 22, no. 4, pp. 2694–2724, 2020.

    Article  Google Scholar 

  40. S. J. J. Kim, S. Park and S. Yoo, “Spatio-temporal split learning,” in 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks-Supplemental Volume (DSN-S). IEEE, 2021, pp. 11–12.

    Google Scholar 

  41. Y. Ha, G. Lee, M. Yoo, S. Jung, S. Yoo, and J. Kim, “Feasibility study of multi-site split learning for privacy-preserving medical systems under data imbalance constraints in COVID-19, X-ray, and cholesterol dataset,” Scientific Reports, vol. 12, p. 1534, January 2022.

    Article  Google Scholar 

  42. V. S. A. Bandyopadhyay, T. S. Roy and S. Mallik, “Combinatorial auction-based fog service allocation mechanism for iot applications,” in 2020 10th International Conference on Cloud Computing, Data Science & Engineering. IEEE, 2020, pp. 518–524.

    Google Scholar 

  43. D. C. Marinescu, A. Paya, J. P. Morrison, and P. Healy, “An Auction-Driven Self-Organizing Cloud Delivery Model,” CoRR, vol. abs/1312.2998, 2013.

    Google Scholar 

  44. B. Coltin and M. Veloso, “Online Pickup and Delivery Planning with Transfers for Mobile Robots,” in Proc. of the IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China, May 2014, pp. 5786–5791.

    Google Scholar 

  45. J. He, D. Zhang, Y. Zhou, and Y. Zhang, “A Truthful Online Mechanism for Collaborative Computation Offloading in Mobile Edge Computing,” IEEE Transactions on Industrial Informatics, vol. 16, no. 7, pp. 4832–4841, July 2020.

    Article  Google Scholar 

  46. R. B. Myerson, “Optimal Auction Design,” Mathematics of Operations Research, vol. 6, no. 1, pp. 58–73, February 1981.

    Article  MathSciNet  MATH  Google Scholar 

  47. K. Zhu, Y. Xu, J. Qian, and D. Niyato, “Revenue-Optimal Auction For Resource Allocation in Wireless Virtualization: A Deep Learning Approach,” IEEE Transactions on Mobile Computing (Early Access), pp. 1–1, September 2020.

    Google Scholar 

  48. N. C. Luong, Z. Xiong, P. Wang, and D. Niyato, “Optimal Auction for Edge Computing Resource Management in Mobile Blockchain Networks: A Deep Learning Approach,” in Proc. of the IEEE International Conference on Communications (ICC), Missouri, USA, May 2018, pp. 1–6.

    Google Scholar 

  49. Y. Hui, Z. S. N. Cheng, Y. Huang, P. Zhao, T. H. Luan, and C. Li, “Secure and Personalized Edge Computing Services in 6G Heterogeneous Vehicular Networks,” IEEE Internet of Things Journal, vol. 9, no. 8, pp. 5920–5931, 2022.

    Article  Google Scholar 

  50. H. L. H. Liang and W. Zhang, “A Combinatorial Auction Resource Trading Mechanism for Cybertwin-Based 6G Network,” IEEE Internet of Things Journal, vol. 8, no. 22, pp. 16 349–16 358, 2021.

    Google Scholar 

  51. Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin, and V. Chandra, “Federated Learning with Non-IID Data,” CoRR, vol. abs/1806.00582, 2018.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Soyi Jung .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Jung, S., Park, S., Son, S.B., Lee, H., Kim, J. (2024). Network Security and Trustworthiness. In: Lin, X., Zhang, J., Liu, Y., Kim, J. (eds) Fundamentals of 6G Communications and Networking. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-37920-8_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-37920-8_30

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37919-2

  • Online ISBN: 978-3-031-37920-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics