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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Sarisht Wadhwa (Duke University), Jannis Stoeter (Duke University), Fan Zhang (Duke University, Yale University), Kartik Nayak (Duke University)

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Tung Le (Virginia Tech), Pengzhi Huang (Cornell University), Attila A. Yavuz (University of South Florida), Elaine Shi (CMU), Thang Hoang (Virginia Tech)

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Jens Christian Dalgaard, Niek A. Janssen, Oksana Kulyuk, Carsten Schurmann (IT University of Copenhagen)

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