Sensor Attack Detection and Classification via CNN and LSTM
نویسندگان
چکیده
In recent years, security of autonomous vehicles is emerging as popular research topics. Especially, autonomous vehicles are equipped with many sensors such as GPS, IMU, wheel encoders and some of them are vulnerable to the attack, such as spoofing. Our objective is to detect and classify attacks of the right encoder sensor by using variables of GPS, IMU, two wheel encoder sensors. We also analyze classification accuracy and computational cost when the data are applied to Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM).
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تاریخ انتشار 2017