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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Detecting CAN Masquerade Attacks with Signal Clustering Similarity

Pablo Moriano (Oak Ridge National Laboratory), Robert A. Bridges (Oak Ridge National Laboratory) and Michael D. Iannacone (Oak Ridge National Laboratory)

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Speeding Dumbo: Pushing Asynchronous BFT Closer to Practice

Bingyong Guo (Institute of Software, Chinese Academy of Sciences), Yuan Lu (Institute of Software Chinese Academy of Sciences), Zhenliang Lu (The University of Sydney), Qiang Tang (The University of Sydney), jing xu (Institute of Software, Chinese Academy of Sciences), Zhenfeng Zhang (Institute of Software, Chinese Academy of Sciences)

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Chosen-Instruction Attack Against Commercial Code Virtualization Obfuscators

Shijia Li (College of Computer Science, NanKai University and the Tianjin Key Laboratory of Network and Data Security Technology), Chunfu Jia (College of Computer Science, NanKai University and the Tianjin Key Laboratory of Network and Data Security Technology), Pengda Qiu (College of Computer Science, NanKai University and the Tianjin Key Laboratory of Network and Data…

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