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Research on security technology to respond to edge router-based network attacks

Edge 라우터 기반 네트워크 공격에 대응하는 보안기술 연구

  • Hwang, Seong-Kyu (Department of Information & Commnincation Engg., Chosun College University of Science & Technology)
  • Received : 2022.08.29
  • Accepted : 2022.09.12
  • Published : 2022.09.30

Abstract

Existing research on security technology related to network attack response has focused on research using hardware network security technology, network attacks that wiretap and wiretap network packets, denial of service attack that consumes server resources to bring down the system, and network by identifying vulnerabilities before attack. It is classified as a scanning attack. In addition, methods for increasing network security, antivirus vaccines and antivirus systems have been mainly proposed and designed. In particular, many users do not fully utilize the security function of the router. In order to overcome this problem, it is classified according to the network security level to block external attacks through layered security management through layer-by-layer experiments. The scope of the study was presented by examining the security technology trends of edge routers, and suggested methods and implementation examples to protect from threats related to edge router-based network attacks.

네트워크 공격 대응에 관한 보안기술의 기존 연구들은 하드웨어적 네트워크 보안 기술을 이용하여 네트워크의 보안성을 높이는 방법이나 바이러스 방역 백신과 바이러스 방역 시스템이 주로 제안 설계되어왔다. 많은 사용자는 라우터의 보안 기능을 충분히 활용하지 못하고 있어 이러한 문제점을 극복하기 위해 네트워크 보안 수준에 따라 분리함으로써 계층화된 보안 관리를 통하여 외부에서의 공격을 차단할 수 있음을 계층별 실험을 통해 분류하였다. 연구의 범위는 Edge 라우터의 보안기술 동향을 살펴봄으로 Edge 라우터 기반의 네트워크 공격에 관한 위협으로부터 보호하는 방법과 구현 사례를 제시한다.

Keywords

References

  1. M. A. Khan and K. Salah, "Iot security: Review, blockchain solutions, and open challenges," Future Generation Computer Systems, vol. 82, pp. 395- 411, May. 2018. https://doi.org/10.1016/j.future.2017.11.022
  2. K. Yang, Q. Li, and L. Sun, "Towards automatic fingerprinting of IoT devices in the cyberspace," Computer Networks, vol. 148, pp. 318-327, Jan. 2019. https://doi.org/10.1016/j.comnet.2018.11.013
  3. Bitdefender, Bitdefender IoT Security Platform [Internet]. Available: https://www.bitdefender.com/iot/.
  4. Fing, Business Solutions: Device Recognition [Internet]. Available: https://www.fing.com/business/.
  5. S. E. Yang, I. S. Kang, B. O. Go, and H. K. Jung, "A Realtime Traffic Shaping Method for VPN Tunneling on Smart Gateway Supporting IoT," The Journal of Korea Institute of Information and Communication Engineering, vol. 21, no. 6, pp. 1121-1126, Jun. 2017.
  6. H. Khelifi, S. Luo, B. Nour, H. Moungla, Y. Faheem, R. Hussain, and A. Ksentini, "Named Data Networking in Vehicular Ad Hoc Networks: State-of-the-Art and Challenges,'' IEEE Communication & Surveys Tutorials, vol. 22, no. 1, pp. 320-351, Mar. 2020 https://doi.org/10.1109/COMST.2019.2894816
  7. M. Lotfollahi, M. J. Siavoshani, R. S. H. Zade, and M. Saberian, "Deep packet: a novel approach for encrypted traffic classification using deep learning," Soft Computing, vol. 24, no. 3, pp. 1999-2012, May. 2019.
  8. G. Aceto, D. Ciuonzo, A. Montieri, and A. Pescape, "Mobile Encrypted Traffic Classification Using Deep Learning," in Proceedings of 2018 Network Traffic Measurement and Analysis Conference (TMA), Vienna, Austria, pp.1-8, 2018.