Deterrence of Intelligent DDoS via Multi-Hop Traffic Divergence

Li, Yuanjie; Li, Hewu; Lv, Zhizheng; Yao, Xingkun; Li, Qianru; Wu, Jianping

doi:10.1145/3460120.3484737

Cited by 7 publications

(1 citation statement)

References 55 publications

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“…Domestic research on satellite network security mainly focuses on DDoS protection and detection. Aiming at protecting satellite Internet from DDoS attacks, Guo et al [ 22 ] proposed a blockchain-based distributed collaborative entrance defense (DCED) framework, with which network traffic characteristics can be recorded and aggregated at the entrances of satellite Internet. The results show that the framework can accurately identify attack traffic in 1500 ms, and the framework is more effective than other similar DDoS methods.…”

Section: Introductionmentioning

confidence: 99%

Security Performance Analysis of LEO Satellite Constellation Networks under DDoS Attack

Zhang

Wang

et al. 2022

Sensors

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Low Earth orbit satellite constellation networks (LSCNs) have attracted significant attention around the world due to their great advantages of low latency and wide coverage, but they also bring new challenges to network security. Distributed denial of service (DDoS) attacks are considered one of the most threatening attack methods in the field of Internet security. In this paper, a space-time graph model is built to identify the key nodes in LSCNs, and a DDoS attack is adopted as the main means to attack the key nodes. The scenarios of two-satellite-key-node and multi-satellite-key-node attacks are considered, and their security performance against DDoS attacks is also analyzed. The simulation results show that the transmission path of key satellite nodes will change rapidly after being attacked, and the average end-to-end delay and packet loss are linearly related to the number of key-node attacks. This work provides a comprehensive analysis of the security performance of LSCNs under a DDoS attack and theoretical support for future research on anti-DDoS attack strategies for LSCNs.

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Section: Introductionmentioning

confidence: 99%