Yangtao Deng (Tsinghua University), Qian Wu (Tsinghua University), Zeqi Lai (Tsinghua University), Chenwei Gu (Tsinghua University), Hewu Li (Tsinghua University), Yuanjie Li (Tsinghua University), Jun Liu (Tsinghua University)

In this paper, we perform a multifaceted study on the security risk involved by the unique time-varying bottleneck links in emerging Low-Earth Orbit (LEO) satellite networks (LSNs). We carry out our study in three steps. First, we profile the spatial and temporal characteristics of bottleneck links and how they might be exploited for bottleneck identification. Thus, the bottleneck links imposes a new risk of link flooding attack (LFA) on LSNs. Second, we propose SKYFALL, a new LFA risk analyzer that enables satellite network operators to simulate various LFA behaviors and comprehensively analyze the consequences on LSN services. Concretely, SKYFALL's analysis based on real-world information of operational LSNs demonstrates that the throughput of legal background traffic could be reduced by a factor of 3.4 if an attacker can manipulate a number of compromised user terminals to continuously congest the bottleneck links. Based on our analysis, we finally discuss the limitations of traditional LFA countermeasures and propose new mitigation strategies for LSNs.

View More Papers

On the Realism of LiDAR Spoofing Attacks against Autonomous...

Takami Sato (University of California, Irvine), Ryo Suzuki (Keio University), Yuki Hayakawa (Keio University), Kazuma Ikeda (Keio University), Ozora Sako (Keio University), Rokuto Nagata (Keio University), Ryo Yoshida (Keio University), Qi Alfred Chen (University of California, Irvine), Kentaro Yoshioka (Keio University)

Read More

DLBox: New Model Training Framework for Protecting Training Data

Jaewon Hur (Seoul National University), Juheon Yi (Nokia Bell Labs, Cambridge, UK), Cheolwoo Myung (Seoul National University), Sangyun Kim (Seoul National University), Youngki Lee (Seoul National University), Byoungyoung Lee (Seoul National University)

Read More

Black-box Membership Inference Attacks against Fine-tuned Diffusion Models

Yan Pang (University of Virginia), Tianhao Wang (University of Virginia)

Read More