一种用于车载网络安全的对抗攻击防御系统
An Adversarial Attack Defending System for Securing In-Vehicle Networks
摘要：
在现代汽车中，有70多个电子控制单元（ECU）。对于车载网络，ECU通过遵循标准通信协议（例如控制器局域网（CAN））相互通信。但是，攻击者可以通过WLAN或蓝牙轻松访问车载网络，从而危及ECU。虽然有各种各样的深度学习（DL）方法被建议用于保护车载网络，但最近对对抗性示例的研究表明，攻击者可以很容易地欺骗DL模型。在这项研究中，我们进一步探索了车载网络中的对抗性示例。我们首先发现并实现了两个对抗攻击模型，它们对车载网络中使用的基于长短期记忆（LSTM）的检测模型有害。然后，我们提出了一个对抗攻击防御系统（AADS），用于保护车载网络。具体而言，我们专注于车载网络中与制动相关的ECU。我们的实验结果表明，对手可以很容易地攻击基于LSTM的检测模型的成功率超过98%，所提出的AADS实现了超过99%的准确率检测对抗攻击。
英文摘要：
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.
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