An authentication scheme to defend against UDP DrDoS attacks in 5G networks
Article
Huang, H., Hu, L., Chu, J. and Cheng, X. 2019. An authentication scheme to defend against UDP DrDoS attacks in 5G networks. IEEE Access. 7, pp. 175970-175979. https://doi.org/10.1109/ACCESS.2019.2957565
| Type | Article |
|---|---|
| Title | An authentication scheme to defend against UDP DrDoS attacks in 5G networks |
| Authors | Huang, H., Hu, L., Chu, J. and Cheng, X. |
| Abstract | 5th generation wireless systems are coming. While we are excited about the delay-free high speeds 5G will bring, security problems are becoming more and more serious. Increasingly rampant Distributed Denial of service (DDoS) attacks, particularly Distributed Reflection Denial of Service (DrDoS) attacks with User Datagram Protocols (UDPs) have developed into a global problem. This article presents a design, implementation, analysis, and experimental evaluation of an authentication scheme, a defense against UDP DrDoS attacks, by which attackers cleverly use rebound server farms to bounce a flood of packets to a target host. We call our solution IEWA because it combines the concepts of increasing expenses and weak authentication. In this paper, we apply IEWA to Network Time Protocol (NTP). First, we simulate and compare the original and improved protocols. Next, we verify the effectiveness of our proposed scheme. We show that our improved scheme is safer than the original scheme. Finally, we compare our solution with existing state-of-the-art schemes, using indicators such as communication overhead, server storage costs, client storage costs, computation costs of server and computation costs of client. We find that our scheme improves system stability and security, reduces communication overhead, server storage cost and computational costs. Our solution not only improves the NTP protocol to mitigate DrDoS attacks, but also strengthens other UDP protocols that are vulnerable to DrDoS attacks. Therefore, our solution can be used as a solution to UDP DrDoS attacks in 5G Networks |
| Keywords | Secure Communication for the Next Generation 5G and IoT Networks |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Journal | IEEE Access |
| ISSN | 2169-3536 |
| Publication dates | |
| Online | 04 Dec 2019 |
| 19 Dec 2019 | |
| Publication process dates | |
| Deposited | 12 Mar 2020 |
| Accepted | 27 Nov 2019 |
| Output status | Published |
| Publisher's version | License |
| Copyright Statement | This work is licensed under a Creative Commons Attribution 4.0 License. |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/ACCESS.2019.2957565 |
| Language | English |
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Guo, X., Huang, Y., Ye, J., Yin, S., Li, M., Li, Z., Yiu, S. and Cheng, X. 2021. ShadowFPE: new encrypted web application solution based on shadow DOM. Mobile Networks and Applications. 26 (4), pp. 1733-1746. https://doi.org/10.1007/s11036-019-01509-yLearning context-aware outfit recommendation
Abugabah, A., Cheng, X. and Wang, J. 2020. Learning context-aware outfit recommendation. Symmetry. 12 (6), pp. 1-13. https://doi.org/10.3390/sym12060873