Electronics and Telecommunications Trends (전자통신동향분석)

Electronics and Telecommunications Research Institute (한국전자통신연구원)
권호 표지
DOI QR코드

DOI QR Code

Study on Trusted Models and Intelligent Intrusion Detection Systems for 6G Mobile Networks

6G 환경을 고려한 트러스트 모델 및 지능형 침입 탐지 기술 동향

  • C.H. Park ;
  • K.M. Park ;
  • J.H. Song ;
  • J.H. Kim ;
  • S.H. Kim
  • 박철희 (인공지능데이터보안연구실) ;
  • 박경민 (인공지능데이터보안연구실) ;
  • 송지현 (인공지능데이터보안연구실) ;
  • 김종현 (인공지능데이터보안연구실) ;
  • 김수형 (인공지능데이터보안연구실)
  • Published : 2024.10.01
Download PDF
⟨ Previous Next ⟩

Abstract

The advent of 6G mobile communication technologies promises to surpass the capabilities of existing 5G by offering ultra high-speed data transmission, ultra low latency, and extensive connectivity, enabling a new wave of digital transformation across various fields. However, the openness and decentralized nature of 6G systems, which enhance their flexibility and scalability, can expand the attack surface and increase security threats from cyber-attacks. In this article, we analyze the current research trends related to security in the 6G mobile communication landscape.

Keywords

Acknowledgement

이 논문은 2024년도 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행된 연구 결과임[No. RS-2024-00444170, 6G 개방형 네트워크 환경에서 트러스트 모델 기반 지능형 침해대응 기술 연구 및 국제협력].

References

  1. Recommendation ITU-R M.2160, Framework and overall objectives of the future development of IMT for 2030 and beyond, Nov. 2023. 
  2. K.B. Letaief et al., "The roadmap to 6G: AI empowered wireless networks," IEEE Commun. Mag., vol. 57, no. 8, 2019, pp. 84-90. 
  3. S. Dang et al., "What should 6G be?," Nature Electron., vol. 3, no. 1, 2020, pp. 20-29. 
  4. M. Ylianttila et al., "6G white paper: Research challenges for trust, security and privacy," arXiv preprint, 2020. arXiv: 2004.11665. 
  5. P. Porambage et al., "The roadmap to 6G security and privacy," IEEE Open J. Commun. Soc., vol. 2, 2021, pp. 1094-1122. 
  6. V.L. Nguyen et al., "Security and privacy for 6G: A survey on prospective technologies and challenges," IEEE Commun. Surv. Tut., vol. 23, no. 4, 2021, pp. 2384-2428. 
  7. B. Veith et al., "The road to trustworthy 6G: A survey on trust anchor technologies," IEEE Open J. Commun. Soc., 4, 2023, pp. 581-595. 
  8. HuaweiTech, "6G native trustworthiness," 2022. 12. 
  9. NGMN, "6G trustworthiness considerations," 2023. 
  10. J.H. Huang et al., "Developing xApps for rogue base station detection in SDR-enabled O-RAN," in IEEE Conf. Comput. Commun. Workshops, (Hoboken, NJ, USA), 2023. 
  11. B.M. Xavier et al., "Machine learning-based early attack detection using open RAN intelligent controller," in IEEE Int. Conf. Commun., (Rome, Italy), 2023. 
  12. srsRAN Project, https://www.srsran.com/5g 
  13. H. Wen et al., "5G-Spector: An O-RAN Compliant Layer-3 Cellular Attack Detection Service," in Proc. 31st Annu. Netw. Distrib. Syst. Secur. Symp. (NDSS '24), San Diego, CA, USA, 2024. 
  14. B. McMahan et al., "Communication-efficient learning of deep networks from decentralized data," in Artificial Intelligence and Statistics, PMLR, 2017, pp. 1273-1282. 
  15. O. Gupta and R. Raskar, "Distributed learning of deep neural network over multiple agents," J. Netw. Comput. Applicat., vol. 116, 2018, pp. 1-8. 
  16. C. Thapa et al., "SplitFed: When federated learning meets split learning," in Proc. AAAI Conf. Artif. Intell. vol. 36, no. 8, 2022, pp. 8485-8493. 
  17. R. Ormandi et al., "Gossip learning with linear models on fully distributed data," Concurr. Comput.: Pract. Exp., vol. 25, no. 4, 2013, pp. 556- 571. 
  18. S.A. Rahman et al., "Internet of things intrusion detection: Centralized, on-device, or federated learning?," IEEE Netw., vol. 34, no. 6, 2020, pp. 310-317. 
  19. T.T. Huong et al., "Lockedge: Low-complexity cyberattack detection in IoT edge computing," IEEE Access, 9, 2021, pp. 29696-29710. 
  20. Q. Qin et al., "Line-speed and scalable intrusion detection at the network edge via federated learning," in IFIP Netw. Conf., (Paris, France) 2020, pp. 352-360. 
  21. T.D. Nguyen et al., "DÏoT: A federated self-learning anomaly detection system for IoT," in IEEE 39th Int. Conf. Distrib. Comput. Syst. (Dallas, TX, USA), 2019, pp. 756-767. 
  22. J. Li et al., "FLEAM: A federated learning empowered architecture to mitigate DDoS in industrial IoT," IEEE Trans. Ind. Inform., vol. 18, no. 6, 2021, pp. 4059-4068. 
  23. T.V. Khoa et al., "Collaborative learning model for cyberattack detection systems in iot industry 4.0," in IEEE Wirel. Commun. Netw. Conf. (Seoul, Rep. of Korea), 2020, pp. 1-6. 
  24. B. Li et al., "DeepFed: Federated deep learning for intrusion detection in industrial cyber-physical systems," IEEE Trans. Industr. Inform., vol. 17, no. 8, 2020, pp. 5615-5624. 
  25. S. Jayasinghe et al., "Federated learning based anomaly detection as an enabler for securing network and service management automation in beyond 5g networks," In Joint Eur. Conf. Netw. Commun. 6G Summit (Grenoble, France), 2022, pp. 345-350. 
  26. C. Park et al., "Distributed learning-based intrusion detection in 5g and beyond networks," in Joint Eur. Conf. Netw. Commun. 6G Summit (Gothenburg, Sweden), 2023, pp. 490-495. 
  27. D. Attanayaka et al., "Peer-to-peer federated learning based anomaly detection for open radio access networks," in IEEE Int. Conf. Commun. (Rome, Italy), 2023, pp. 5464-5470. 
  28. Report ITU-RM. 2410 (11/2017), "Minimum requirements related to technical performance for IMT-2020 radio interfaces," 2017. 
  29. 3GPP TR 38.843, "Study on Artificial Intelligence(AI)/Machine Learning(ML) for NR air interface(Release 18)," 2023. 
  30. https://www.etnews.com/20240712000173 
  31. ITU-T SG17 Homepage, htps:/ww.itu.int/en/ITU-T/studygroups/2017-20/17/Pages/default.aspx