The communication and connectivity functions of vehicles increase their vulnerability to hackers. The unintended failure and malfunction of in-vehicle systems caused by external factors threaten the security and safety of passengers. As the controller area network alone cannot protect vehicles from external attacks, techniques to analyze and detect external attacks are required. Therefore, we propose a multi-labeled hierarchical classification (MLHC) intrusion detection model that analyzes and detects external attacks caused by message injection. This model quickly determines the occurrence of attacks and classifies the attack using only existing classified attack data. We evaluated the performance of the model by analyzing its learning space. We further verified the model by comparing its accuracy, F1 score and data learning and evaluation times with the two layers multi-class detection (TLMD) and single-layer multi-class classification (SLMC) models. The simulation results show that the MLHC model has the highest F1 score of 0.9995 and is 87.30% and 99.92% faster than the SLMC and TLMD models in terms of detection time, respectively. Consequently, the proposed model can classify both the type and existence or absence of attacks with high accuracy and can be used in interior communication environments of high-speed vehicles with a high throughput.

中文翻译：

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使用机器学习算法的车载网络分层异常检测模型。

车辆的通信和连接功能增加了其对黑客的脆弱性。外部因素导致的车载系统意外故障和失灵威胁着乘客的安全性。由于仅控制器局域网无法保护车辆免受外部攻击，因此需要用于分析和检测外部攻击的技术。因此，我们提出了一种多标签分层分类（MLHC）入侵检测模型，该模型可以分析和检测由消息注入引起的外部攻击。该模型可以快速确定攻击的发生，并仅使用现有的分类攻击数据对攻击进行分类。我们通过分析模型的学习空间来评估模型的性能。我们通过比较模型的准确性进一步验证了模型，F1评分和数据学习与评估时间采用两层多层检测（TLMD）和单层多层分类（SLMC）模型。仿真结果表明，MLHC模型的最高F1得分为0.9995，在检测时间方面分别比SLMC和TLMD模型快87.30％和99.92％。因此，所提出的模型可以高精度地对攻击的类型和存在与否进行分类，并且可以在具有高吞吐量的高速车辆的内部通信环境中使用。