基于卷积神经网络的汽车以太网AVTP流入侵检测系统
Convolutional Neural Network-based Intrusion Detection System for AVTP Streams in Automotive Ethernet-based Networks
摘要：
互联和自动驾驶汽车（CAV）是传统汽车的创新形式。汽车以太网取代了控制器局域网和FlexRay，以支持高清应用所需的大吞吐量。由于CAV具有多种功能，它们表现出较大的攻击面和增加的易受攻击性。然而，没有以前的研究集中在基于以太网的汽车网络的入侵检测。在本文中，我们提出了一种入侵检测方法检测音频视频传输协议（AVTP）流注入攻击在汽车以太网为基础的网络。据我们所知，这是第一个为汽车以太网开发的这种方法。提出的入侵检测模型是基于特征生成和卷积神经网络（CNN）。为了评估我们的入侵检测系统，我们建立了一个物理的BroadR-Reach的测试床，并捕获真正的AVTP数据包。实验结果表明，该模型具有优异的性能：F1分数和召回率分别大于0.9704和0.9949。根据每个输入的推理时间和AVTP流量的生成间隔，我们的CNN模型可以很容易地用于实时检测。
英文摘要：
Connected and autonomous vehicles (CAVs) are an innovative form of traditional vehicles. Automotive Ethernet replaces the controller area network and FlexRay to support the large throughput required by high-definition applications. As CAVs have numerous functions, they exhibit a large attack surface and an increased vulnerability to attacks. However, no previous studies have focused on intrusion detection in automotive Ethernet-based networks. In this paper, we present an intrusion detection method for detecting audio-video transport protocol (AVTP) stream injection attacks in automotive Ethernet-based networks. To the best of our knowledge, this is the first such method developed for automotive Ethernet. The proposed intrusion detection model is based on feature generation and a convolutional neural network (CNN). To evaluate our intrusion detection system, we built a physical BroadR-Reach-based testbed and captured real AVTP packets. The experimental results show that the model exhibits outstanding performance: the F1-score and recall are greater than 0.9704 and 0.9949, respectively. In terms of the inference time per input and the generation intervals of AVTP traffic, our CNN model can readily be employed for real-time detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
