Pritam Dash (University of British Columbia) and Karthik Pattabiraman (University of British Columbia)

Robotic Vehicles (RV) rely extensively on sensor inputs to operate autonomously. Physical attacks such as sensor tampering and spoofing feed erroneous sensor measurements to deviate RVs from their course and result in mission failures. We present PID-Piper , a novel framework for automatically recovering RVs from physical attacks. We use machine learning (ML) to design an attack resilient FeedForward Controller (FFC), which runs in tandem with the RV’s primary controller and monitors it. Under attacks, the FFC takes over from the RV’s primary controller to recover the RV, and allows the RV to complete its mission successfully. Our evaluation on 6 RV systems including 3 real RVs shows that PID-Piper allows RVs to complete their missions successfully despite attacks in 83% of the cases.

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Andreas Zeller (CISPA Helmholtz Center for Information Security)

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PHYjacking: Physical Input Hijacking for Zero-Permission Authorization Attacks on...

Xianbo Wang (The Chinese University of Hong Kong), Shangcheng Shi (The Chinese University of Hong Kong), Yikang Chen (The Chinese University of Hong Kong), Wing Cheong Lau (The Chinese University of Hong Kong)

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First, Fuzz the Mutants

Alex Groce (Northern Arizona Univerisity), Goutamkumar Kalburgi (Northern Arizona Univerisity), Claire Le Goues (Carnegie Mellon University), Kush Jain (Carnegie Mellon University), Rahul Gopinath (Saarland University)

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Clarion: Anonymous Communication from Multiparty Shuffling Protocols

Saba Eskandarian (University of North Carolina at Chapel Hill), Dan Boneh (Stanford University)

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