Sensor Spoofing Detection On Autonomous Vehicle Using Channel-spatial-temporal Attention Based Autoencoder Network

Abstract

Autonomous vehicles rely on various sensors to evaluate the driving environment and issue essential control commands. Nonetheless, these sensors are susceptible to false data injection and spoofing attacks, which could easily be launched wirelessly and remotely by attackers. This paper proposes a channel-spatial-temporal attention-based autoencoder network to detect sensor spoofing attacks on autonomous vehicles. The network utilizes the reconstruction error based on the autoencoder to detect abnormalities in input time series data of multiple sensors. The proposed model consists of a memory-augmented based spatial-attention block and PSE-Res2Net block based encoder and decoder. PSE-Res2Net block initially adopts Res2Net module to generate multi-scale feature graph and enhance multi-dimensional representation ability of neural network, then applies the PSENet module to capture location-aware channel information and channel-sensitive spatial information through the interaction of channel attention and spatial attention. Moreover, the memory-augmented based temporal-attention block is developed to integrate multi-scale features and gather global sequence information of sensor measurements. The results of the experimental evaluation on the comma2k19, KITTI, and CCSAD datasets illustrate that the proposed detection model is superior to the baseline technologies in terms of mPre, mRec, mF1 score and achieves stronger robustness against noise.