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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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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Demo #2: Policy-based Discovery and Patching of Logic Bugs...

Hyungsub Kim (Purdue University), Muslum Ozgur Ozmen (Purdue University), Antonio Bianchi (Purdue University), Z. Berkay Celik (Purdue University) and Dongyan Xu (Purdue University)

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Demo #13: Attacking LiDAR Semantic Segmentation in Autonomous Driving

Yi Zhu (State University of New York at Buffalo), Chenglin Miao (University of Georgia), Foad Hajiaghajani (State University of New York at Buffalo), Mengdi Huai (University of Virginia), Lu Su (Purdue University) and Chunming Qiao (State University of New York at Buffalo)

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Vision-Based Two-Factor Authentication & Localization Scheme for Autonomous Vehicles

Anas Alsoliman, Marco Levorato, and Qi Alfred Chen (UC Irvine)

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