Yulong Cao, Jiaxiang Ma, Kevin Fu (University of Michigan), Sara Rampazzi (University of Florida), and Z. Morley Mao (University of Michigan)

Best Demo Award Runner-up ($200 cash prize)!

Recent studies have demonstrated that LiDAR sensors are vulnerable to spoofing attacks, in which adversaries spoof fake points to fool the car’s perception system to see nonexistent obstacles. However, these attacks are generally conducted on static or simulated scenarios. Therefore, in this demo, we perform the first LiDAR spoofing attack on moving targets. We implemented a minimal tracking system integrated with the spoofer device to perform laser-based attacks on Lidar sensors. The demo shows how it is possible to inject up to 100 fake cloud points under three different scenarios.

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POSEIDON: Privacy-Preserving Federated Neural Network Learning

Sinem Sav (EPFL), Apostolos Pyrgelis (EPFL), Juan Ramón Troncoso-Pastoriza (EPFL), David Froelicher (EPFL), Jean-Philippe Bossuat (EPFL), Joao Sa Sousa (EPFL), Jean-Pierre Hubaux (EPFL)

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Refining Indirect Call Targets at the Binary Level

Sun Hyoung Kim (Penn State), Cong Sun (Xidian University), Dongrui Zeng (Penn State), Gang Tan (Penn State)

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Detecting Tor Bridge from Sampled Traffic in Backbone Networks

Hua Wu (School of Cyber Science & Engineering and Key Laboratory of Computer Network and Information Integration Southeast University, Ministry of Education, Jiangsu Nanjing, Purple Mountain Laboratories for Network and Communication Security (Nanjing, Jiangsu)), Shuyi Guo, Guang Cheng, Xiaoyan Hu (School of Cyber Science & Engineering and Key Laboratory of Computer Network and Information Integration…

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User Expectations and Understanding of Encrypted DNS Settings

Alexandra Nisenoff, Nick Feamster, Madeleine A Hoofnagle†, Sydney Zink. (University of Chicago and †Northwestern)

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