Li Yue, Zheming Li, Tingting Yin, and Chao Zhang (Tsinghua University)

Modern vehicles have many electronic control units (ECUs) connected to the Controller Area Network (CAN) bus, which have few security features in design and are vulnerable to cyber attacks. Researchers have proposed solutions like intrusion detection systems (IDS) to mitigate such threats. We presented a novel attack, CANCloak, which can deceive two ECUs with one CAN data frame, and therefore can bypass IDS detection or cause vehicle malfunction. In this attack, assuming a malicious transmitter is controlled by the adversary, one crafted CAN data frame can be transmitted to a target receiver, while other ECUs shall not receive that frame nor raise any error. We have setup a physical test environment and evaluated the effectiveness of this attack. Evaluation results showed that success rate of CANCloak reaches up to 99.7%, while the performance depends on the attack payload and sample point settings of victim receivers, independent from bus bit rate.

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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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Jeremy Daily, David Nnaji, and Ben Ettlinger (Colorado State University)

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Henry Xu, An Ju, and David Wagner (UC Berkeley) Baidu Security Auto-Driving Security Award Winner ($1000 cash prize)!

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Anway Mukherjee, Ryan Gerdes, and Tam Chantem (Virginia Tech)

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