DOI QR코드

DOI QR Code

Network Traffic-Based Access Control Using Software-Defined Perimeter

소프트웨어 정의 경계를 이용한 네트워크 트래픽 기반 동적 접근 제어

  • Seo-Yi Kim (Sungshin Women's University) ;
  • Il-Gu Lee (Sungshin Women's University)
  • 김서이 (성신여자대학교) ;
  • 이일구 (성신여자대학교)
  • Received : 2024.06.12
  • Accepted : 2024.07.17
  • Published : 2024.08.31

Abstract

The rapid advancement of computer technology has necessitated a safer user environment, prompting the adoption of the zero trust model, which verifies all internal and external network activities. This paper proposes an efficient network traffic data-based dynamic access control method leveraging Software-Defined Perimeter (SDP) capabilities to implement zero trust and address latency issues. According to the performance evaluation results, the detection performance of the proposed scheme is similar to that of conventional schemes, but the dataset size was reduced by 62.47%. Additionally, by proposing an adaptive zero trust verification approach, the dataset size and verification time were reduced by 83.9% and 9.1%, respectively, while maintaining similar detection performance to conventional methods.

컴퓨터 기술의 급속한 발전은 더 안전한 사용자 환경이 필요하게 되어, 모든 내부 및 외부 네트워크 활동을 검증하는 제로 트러스트 모델의 도입을 촉진했다. 본 논문은 제로 트러스트의 구현 및 지연 문제를 해결하기 위해 소프트웨어 정의 경계 기능을 활용한 효율적인 네트워크 트래픽 데이터 기반 동적 접근 제어 방법을 제안한다. 성능 평가 결과에 따르면 제안한 방법의 탐지 성능은 기존 방식과 유사하게 나타났지만 데이터 셋의 크기는 약 70% 감소했다. 그리고 적응형 제로 트러스트 검증 방식을 제안하여 데이터 셋 크기와 검증 시간을 각각 약 83%, 10% 줄이면서 종래의 방식과 유사한 탐지 성능을 유지했다.

Keywords

Acknowledgement

본 논문은 2024년도 산업통상자원부 및 한국산업기술진흥원의 산업혁신인재성장지원사업(RS-2024-00415520) 과 과학기술정보통신부 및 정보통신기획평가원의 ICT혁신인재4.0 사업의 연구결과로 수행되었음 (No.IITP-2022-RS-2022-00156310)

References

  1. Syed, N. F., Shah, S. W., Shaghaghi, A., Anwar, A., Baig, Z., & Doss, R., "Zero trust architecture (zta): A comprehensive survey," IEEE Access, Volume: 10, pp.57143-57179, 12 May 2022.
  2. Teerakanok, S., Uehara, T., Inomata, A., "Migrating to zero trust architecture: Reviews and challenges," Security and Communication Networks, pp. 1-10, 25 May 2021
  3. TN, N., Pramod, D., Singh, R., "Zero trust security model: Defining new boundaries to organizational network," In Proceedings of the 2023 Fifteenth International Conference on Contemporary Computing, pp. 603-609, August. 2023
  4. So-Hui Kim, Il-Gu Lee, Ye-Eun Lee, "Practical Zero Trust Technology and Policy for 5G Network Convergence Industry," Korean Journal of Industrial Security, Vol. 14, pp.203-222, Jan. 2024
  5. Fernandez, Eduardo B., and Andrei Brazhuk, "A critical analysis of Zero Trust Architecture (ZTA)," Computer Standards & Interfaces, volume: 89,103832, April. 2024
  6. Zolotukhin, M., Hamalainen, T., & Kotilainen, P., "Intelligent solutions for attack mitigation in zero-trust environments. In Cyber Security: Critical Infrastructure Protection", Cham: Springer International Publishing, COMPUTMETHODS, volume 56, pp. 403-417, 2022
  7. S. Rose, O. Borchert, S. Mitchell, S. Connelly, "Zero Trust Architecture, "NIST Special Publication 800-207,National Institute of Standards and Technology, U.S. Department of Commerce, August 2020.
  8. Park, C. S., "Zero Trust Architecture. "Review of Korea Institute of Information Security and Cryptology(KIISC), volume. 33, No. 4, pp.131-141, August 2023
  9. Lee. J. Y, Choi. B. H, Koh. N, Chun. S, "A Study on the Establishment of Zero Trust Security Model," Korea Information Processing Society/Computer and Communication System, volume: 12, No. 6, pp.189-196, June 2023
  10. Devarakonda, A., Sharma, N., Saha, P., & Ramya, S., "Network intrusion detection: A comparative study of four classifiers using the NSL-KDD and KDD'99 datasets," In Journal of Physics: Conference Series, IOP Publishing, Vol. 2161, No. 1, p. 012043, 2022
  11. Yao, Q., Wang, Q., Zhang, X., Fei, J., "Dynamic access control and authorization system based on zero-trust architecture," In Proceedings of the 2020 1st international conference on control, robotics and intelligent system, pp. 123-127, October. 2020
  12. Jin, Q., Wang, L., "Zero-trust based distributed collaborative dynamic access control scheme with deep multi-agent reinforcement learning," EAI Endorsed Transactions on Security and Safety, 27 August 2022
  13. Jeon, So-Eun, et al. "Suboptimal Feature Selection Techniques for Effective Malicious Traffic Detection on Lightweight Devices." CMES-Computer Modeling in Engineering & Sciences, 140(2), May. 2024
  14. Gouveia, A., Correia, M., "Network intrusion detection with XGBoost," In Recent Advances in Security, Privacy, and Trust for Internet of Things (IoT) and Cyber-Physical Systems (CPS), Chapman and Hall/CRC, pp. 137-166, 2020
  15. Deng, Y., Lumley, T, "Multiple imputation through xgboost", Journal of Computational and Graphical Statistics, volume: 33, No: 2 pp.1-19, October. 2023
  16. M. Tavallaee, E. Bagheri, W. Lu, and A. Ghorbani, "A Detailed Analysis of the KDD CUP 99 Data Set," 2009 IEEE symposium on computational intelligence for security and defense applications (CISDA), p. 1-6, July. 2009
  17. Iman Sharafaldin, Arash Habibi Lashkari, and Ali A. Ghorbani, "Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization", 4th International Conference on Information Systems Security and Privacy (ICISSP), Portugal, January 2018