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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When DNS Goes Dark: Understanding Privacy and Shaping Policy...

Vijay k. Gurbani and Cynthia Hood ( Illinois Institute of Technology), Anita Nikolich (University of Illinois), Henning Schulzrinne (Columbia University) and Radu State (University of Luxembourg)

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MINOS: A Lightweight Real-Time Cryptojacking Detection System

Faraz Naseem (Florida International University), Ahmet Aris (Florida International University), Leonardo Babun (Florida International University), Ege Tekiner (Florida International University), A. Selcuk Uluagac (Florida International University)

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