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- W3136139905 abstract "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. As shown in our experiments, adversaries can attack the LSTM-based detection model with a success rate of over 98%. 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 extensive experimental results demonstrate that the proposed AADS achieves over 99 % accuracy for detecting adversarial attacks." @default.
- W3136139905 created "2021-03-29" @default.
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- W3136139905 date "2021-01-09" @default.
- W3136139905 modified "2023-09-27" @default.
- W3136139905 title "An Adversarial Attack Defending System for Securing In-Vehicle Networks" @default.
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- W3136139905 doi "https://doi.org/10.1109/ccnc49032.2021.9369569" @default.
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