华盛顿大学(University of Washington)创建于1861年,坐落在美国最适宜居住和工作的城市西雅图,是美国西海岸最古老的大学,是一所世界顶尖的著名大学,长期保持世界大学财政支出和研究经费前三位。华盛顿大学拥有世界最顶尖的教师队伍,拥有29,804名教职员工,包括5803名教师,师生比例为 1:7.3 ,其中众多教授为所在学术领域的世界领导者。

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主题: Visualizing the Impact of Feature Attribution Baselines

摘要: 路径属性方法是一种基于梯度的解释深度模型的方法。这些方法需要选择一个称为基线输入的超参数。这个超参数是什么意思,它有多重要?本文以图像分类网络为例,对这些问题进行了研究。我们讨论了选择基线输入的几种不同方法以及每个基线中隐含的假设。虽然我们在这里关注的是路径归属方法,但我们对基线的讨论与特征空间中的缺失概念密切相关——这一概念对可解释性研究至关重要。

作者简介: Pascal Sturmfels,华盛顿大学博士,他的研究方向是开发使机器学习模型更易于解释的方法,并应用这些方法来理解生物和健康数据。个人主页:https://psturmfels.github.io/

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In recent years, the security of automotive Cyber-Physical Systems (CPSs) is facing urgent threats due to the widespread use of legacy in-vehicle communication systems. As a representative legacy bus system, the Controller Area Network (CAN) hosts Electronic Control Units (ECUs) that are crucial vehicle functioning. In this scenario, malicious actors can exploit CAN vulnerabilities, such as the lack of built-in authentication and encryption schemes, to launch CAN bus attacks with life-threatening consequences (e.g., disabling brakes). In this paper, we present TACAN (Transmitter Authentication in CAN), which provides secure authentication of ECUs on the legacy CAN bus by exploiting the covert channels, without introducing CAN protocol modifications or traffic overheads. TACAN turns upside-down the originally malicious concept of covert channels and exploits it to build an effective defensive technique that facilitates transmitter authentication via a centralized, trusted Monitor Node. TACAN consists of three different covert channels for ECU authentication: 1) the Inter-Arrival Time (IAT)-based; 2) the Least Significant Bit (LSB)-based; and 3) a hybrid covert channel, exploiting the combination of the first two. In order to validate TACAN, we implement the covert channels on the University of Washington (UW) EcoCAR (Chevrolet Camaro 2016) testbed. We further evaluate the bit error, throughput, and detection performance of TACAN through extensive experiments using the EcoCAR testbed and a publicly available dataset collected from Toyota Camry 2010. We demonstrate the feasibility of TACAN and the effectiveness of detecting CAN bus attacks, highlighting no traffic overheads and attesting the regular functionality of ECUs.

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