V-PSC: A Perturbation-Based Causative Attack Against DL Classifiers' Supply Chain in VANET

Yi Zeng, Meikang Qiu, Jingqi Niu, Yanxin Long, Jian Xiong, Meiqin Liu

Abstract: DL Based classifiers can attain a higher accuracy with less storage requirement, which suits perfectly with the VANET. However, it has been proved that DL models suffer from crafted perturbation data, a small amount of such can misguide the classifier, thus backdoors can be created for malicious reasons. This paper studies such a causative attack in the VANET. We present a perturbation-based causative attack which targets at the supply chain of DL classifiers in the VANET. We first train a classifier using VANET simulated data which meets the standard accuracy for identifying malicious traffic in the VANET. Then, we elaborate on the effectiveness of our presented attack scheme on this pre-trained classifier. We also explore some feasible approaches to ease the outcome brought by our attack. Experimental results show that the scheme can cause the target DL model a 10.52% drop in accuracy.