当前位置: X-MOL 学术Veh. Commun. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Convolutional neural network-based intrusion detection system for AVTP streams in automotive Ethernet-based networks
Vehicular Communications ( IF 6.7 ) Pub Date : 2021-02-02 , DOI: 10.1016/j.vehcom.2021.100338
Seonghoon Jeong , Boosun Jeon , Boheung Chung , Huy Kang Kim

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.



中文翻译:

基于卷积神经网络的基于汽车以太网的AVTP流入侵检测系统

互联自动驾驶汽车(CAV)是传统汽车的一种创新形式。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,因此它们具有较大的攻击面,并且容易受到攻击。但是,以前没有研究集中在基于汽车以太网的网络中的入侵检测上。在本文中,我们提出了一种入侵检测方法,用于检测基于汽车以太网的音频-视频传输协议(AVTP)流注入攻击。据我们所知,这是为汽车以太网开发的第一种此类方法。提出的入侵检测模型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,我们构建了一个基于BroadR-Reach的物理测试平台,并捕获了实际的AVTP数据包。实验结果表明,该模型具有出色的性能:F1得分和召回率分别大于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。

更新日期:2021-02-12
down
wechat
bug