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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Demo #6: Attacks on CAN Error Handling Mechanism

Khaled Serag (Purdue University), Vireshwar Kumar (IIT Delhi), Z. Berkay Celik (Purdue University), Rohit Bhatia (Purdue University), Mathias Payer (EPFL) and Dongyan Xu (Purdue University)

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DRIVETRUTH: Automated Autonomous Driving Dataset Generation for Security Applications

Raymond Muller (Purdue University), Yanmao Man (University of Arizona), Z. Berkay Celik (Purdue University), Ming Li (University of Arizona) and Ryan Gerdes (Virginia Tech)

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Demo #1: Curricular Reinforcement Learning for Robust Policy in...

Yunzhe Tian, Yike Li, Yingxiao Xiang, Wenjia Niu, Endong Tong, and Jiqiang Liu (Beijing Jiaotong University)

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