Jun Ying (Purdue University), Yiheng Feng (Purdue University), Qi Alfred Chen (University of California, Irvine), Z. Morley Mao (University of Michigan)

Intersection movement assist (IMA) is a connected vehicle (CV) application to improve vehicle safety. GPS spoofing attack is one major threat to the IMA application since inaccurate localization results may generate fake warnings that increase rear-end crashes, or cancel real warnings that may lead to angle or swipe crashes. In this work, we first develop a GPS spoofing attack model to trigger the IMA warning of entry vehicles at a roundabout driving scenario. The attack model can generate realistic trajectories while achieving the attack goal. To defend against such attacks, we further design a one-class classifier to distinguish the normal vehicle trajectories from the trajectories under attack. The proposed model is validated with a real-world data set collected from Ann Arbor, Michigan. Results show that although the attack model triggers the IMA warning in a short time (i.e., in a few seconds), the detection model can still identify the abnormal trajectories before the attack succeeds with low false positive and false negative rates.

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Yanzuo Chen (The Hong Kong University of Science and Technology), Yuanyuan Yuan (The Hong Kong University of Science and Technology), Shuai Wang (The Hong Kong University of Science and Technology)

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Tianyang Chen (Huazhong University of Science and Technology), Peng Xu (Huazhong University of Science and Technology), Stjepan Picek (Radboud University), Bo Luo (The University of Kansas), Willy Susilo (University of Wollongong), Hai Jin (Huazhong University of Science and Technology), Kaitai Liang (TU Delft)

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