TENET: Temporal CNN with Attention for Anomaly Detection in Automotive Cyber-Physical Systems
TENET: Temporal CNN with Attention for Anomaly Detection in Automotive Cyber-Physical Systems
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DOI：
10.1109/asp-dac52403.2022.9712524
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发表时间：
2021-09
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通讯作者：
S. V. Thiruloga;Vipin Kumar Kukkala;S. Pasricha
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作者：
S. V. Thiruloga;Vipin Kumar Kukkala;S. Pasricha
现代车辆具有多个电子控制单元（ECU），这些ECU连接在一起作为复杂的分布式网络物理系统（CPS）的一部分。ECU与外部电子系统之间的通信不断增加，使得这些车辆特别容易受到各种网络攻击。在这项工作中，我们提出了一种新的异常检测框架，称为TENET检测车辆上的网络攻击引起的异常。TENET使用具有集成注意力机制的时间卷积神经网络来学习遍历车载网络的消息之间的依赖关系。在车辆中部署后，TENET采用稳健的定量度量和分类器以及学习的依赖关系来检测异常模式。TENET能够在假阴性率方面实现32.70%的改进，在马修斯相关系数方面实现19.14%的改进，在ROC-AUC度量方面实现17.25%的改进，与汽车异常检测方面表现最好的先前工作相比，模型参数减少了94.62%，推理时间减少了48.14%。
Modern vehicles have multiple electronic control units (ECUs) that are connected together as part of a complex distributed cyber-physical system (CPS). The ever-increasing communication between ECUs and external electronic systems has made these vehicles particularly susceptible to a variety of cyber-attacks. In this work, we present a novel anomaly detection framework called TENET to detect anomalies induced by cyber-attacks on vehicles. TENET uses temporal convolutional neural networks with an integrated attention mechanism to learn the dependency between messages traversing the in-vehicle network. Post deployment in a vehicle, TENET employs a robust quantitative metric and classifier, together with the learned dependencies, to detect anomalous patterns. TENET is able to achieve an improvement of 32.70% in False Negative Rate, 19.14% in the Mathews Correlation Coefficient, and 17.25% in the ROC-AUC metric, with 94.62% fewer model parameters, and 48.14% lower inference time compared to the best performing prior works on automotive anomaly detection.