Toward Protecting 5G Sidelink Scheduling in C-V2X Against Intelligent DoS Attacks

被引:8
|
作者
Twardokus, Geoff [1 ,2 ]
Rahbari, Hanif [1 ,2 ]
机构
[1] Rochester Inst Technol, Elect & Comp Engn PhD Program, Rochester, NY 14623 USA
[2] Rochester Inst Technol, ESL Global Cybersecur Inst, Rochester, NY 14623 USA
关键词
5G mobile communication; Vehicle-to-everything; Sensors; 3GPP; Wireless communication; Protocols; Denial-of-service attack; V2V security; denial-of-service; selective jamming; resource scheduling; 5G;
D O I
10.1109/TWC.2023.3249665
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
5G Cellular Vehicle-to-Everything (5G C-V2X) is emerging as the globally dominant connected vehicle technology. One critical application of 5G C-V2X is the direct exchange of safety-critical messages between vehicles to prevent crashes and correspondingly reduce roadway injuries and fatalities. While current C-V2X security protocols concern only message payloads, we expose vulnerabilities in the physical-layer attributes and decentralized MAC-layer scheduling algorithm of 5G C-V2X by developing two stealthy denial-of-service (DoS) attacks to exploit them. These low-duty-cycle attacks dramatically degrade C-V2X availability, increasing the likelihood of prolonged travel times and even vehicle crashes. We further develop detection and mitigation techniques for each attack, in part by exploiting new C-V2X features of 3GPP Rel-17. We experimentally evaluate our attacks and countermeasures in a hardware testbed composed of USRPs and state-of-the-art C-V2X kits as well as through extensive network and roadway simulations, showing that within seconds of initiation our attacks can reduce a target's packet delivery ratio by 90% or that of the C-V2X channel to under 25%. We further evaluate our machine-learning detection and low-cost mitigation techniques, showing the latter completely thwart one attack and reduce the impact of the other by 80%, providing insight towards developing a more robust 5G C-V2X.
引用
收藏
页码:7273 / 7286
页数:14
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