  In a modern vehicle, there are over seventy Electronics Control Units (ECUs).
For an in-vehicle network, ECUs communicate with each other by following a
standard communication protocol, such as Controller Area Network (CAN).
However, an attacker can easily access the in-vehicle network to compromise
ECUs through a WLAN or Bluetooth. Though there are various deep learning (DL)
methods suggested for securing in-vehicle networks, recent studies on
adversarial examples have shown that attackers can easily fool DL models. In
this research, we further explore adversarial examples in an in-vehicle
network. We first discover and implement two adversarial attack models that are
harmful to a Long Short Term Memory (LSTM)-based detection model used in the
in-vehicle network. Then, we propose an Adversarial Attack Defending System
(AADS) for securing an in-vehicle network. Specifically, we focus on
brake-related ECUs in an in-vehicle network. Our experimental results
demonstrate that adversaries can easily attack the LSTM-based detection model
with a success rate of over 98%, and the proposed AADS achieves over 99%
accuracy for detecting adversarial attacks.
