论文标题
汽车系统的安全性和解释性
Security and Interpretability in Automotive Systems
论文作者
论文摘要
缺乏任何发件人身份验证机制使CAN（控制器区域网络）容易受到安全威胁的影响。例如，攻击者可以在公共汽车上冒充ECU（电子控制单元），并使用模仿的ECU的标识符毫不客气地发送欺骗消息。为了解决系统的不安全性质，本论文展示了一种发件人身份验证技术，该技术使用电子控制单元（ECU）和分类模型的功耗测量来确定ECUS的传输状态。该方法在现实世界中的评估表明，该技术适用于广泛的操作条件，并且可以达到良好的准确性。 基于机器学习的安全控制的主要挑战是误报的潜力。假阳性警报可能会引起操作员的恐慌，导致不正确的反应，从长远来看会导致警报疲劳。对于在这种情况下可靠的决策，了解异常模型行为的原因至关重要。但是，这些模型的黑框性质使它们无法解释。因此，本论文的另一个贡献探讨了类型图像和时间序列输入的解释技术，（1）基于其对目标类别的敏感性将权重分配给单个输入，（2），并通过使用生成模型来重建输入区域的敏感区域来量化解释的变化。 总而言之，本论文（https://uwspace.uwaterloo.ca/handle/10012/18134）提出了解决汽车系统中安全性和解释性的方法，这些方法也可以应用于安全，透明和可靠的决策至关重要的其他设置。
The lack of any sender authentication mechanism in place makes CAN (Controller Area Network) vulnerable to security threats. For instance, an attacker can impersonate an ECU (Electronic Control Unit) on the bus and send spoofed messages unobtrusively with the identifier of the impersonated ECU. To address the insecure nature of the system, this thesis demonstrates a sender authentication technique that uses power consumption measurements of the electronic control units (ECUs) and a classification model to determine the transmitting states of the ECUs. The method's evaluation in real-world settings shows that the technique applies in a broad range of operating conditions and achieves good accuracy. A key challenge of machine learning-based security controls is the potential of false positives. A false-positive alert may induce panic in operators, lead to incorrect reactions, and in the long run cause alarm fatigue. For reliable decision-making in such a circumstance, knowing the cause for unusual model behavior is essential. But, the black-box nature of these models makes them uninterpretable. Therefore, another contribution of this thesis explores explanation techniques for inputs of type image and time series that (1) assign weights to individual inputs based on their sensitivity toward the target class, (2) and quantify the variations in the explanation by reconstructing the sensitive regions of the inputs using a generative model. In summary, this thesis (https://uwspace.uwaterloo.ca/handle/10012/18134) presents methods for addressing the security and interpretability in automotive systems, which can also be applied in other settings where safe, transparent, and reliable decision-making is crucial.
