Due to the deployment of 5G technology, the number of IoV (Internet of Vehicle) devices connected to the Internet will explosively grow. However, as a kind of edge network device, IoV devices also face some problems including weak password authentication, lack of security protection, and lagged firmware updating, which largely threaten the security and legitimacy of these devices. IoV device identification is important in discovering, monitoring, and protecting these devices. Although existing proactive identification methods based on device fingerprints can be used to identify the large-scale Internet-connected IoV devices, they can not meet the fine-grained requirements for security risk assessment. Due to the increase in the types and brands of IoV devices, the fingerprint granularity will be insufficient. In this paper, we proposed a retransmission-based TCP fingerprints for large-scale fine-grained proactive device identification. Firstly, a probing scheme was designed to obtain TCP retransmission packet and increase the granularity space of traditional TCP fingerprint by selecting the multi-group features of TCP retransmission messages. Then, according to the bagging strategy of ensemble learning, a combined classifier with five predominant machine learning algorithms was generated. The experimental results showed that the identification accuracy and recall of IoV devices respectively reached 96.7% and 95.2%.

中文翻译：

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用于细粒度 IoV 边缘设备识别的基于重传的 TCP 指纹

由于5G技术的部署，接入互联网的IoV（车联网）设备数量将呈爆发式增长。然而，作为边缘网络设备的车联网设备也面临着密码认证弱、缺乏安全防护、固件更新滞后等问题，在很大程度上威胁着这些设备的安全性和合法性。IoV 设备识别对于发现、监控和保护这些设备非常重要。现有的基于设备指纹的主动识别方法虽然可以用于大规模联网车联网设备的识别，但无法满足安全风险评估的细粒度要求。由于车联网设备种类和品牌的增加，指纹粒度会不足。在本文中，我们提出了一种基于重传的 TCP 指纹，用于大规模细粒度的主动设备识别。首先，设计了一种探测方案，通过选择TCP重传消息的多组特征，获取TCP重传包，增加传统TCP指纹的粒度空间。然后，根据集成学习的 bagging 策略，生成了具有五种主要机器学习算法的组合分类器。实验结果表明，车联网设备的识别准确率和召回率分别达到了96.7%和95.2%。设计了一种探测方案，通过选择TCP重传消息的多组特征，获取TCP重传包，增加传统TCP指纹的粒度空间。然后，根据集成学习的 bagging 策略，生成了具有五种主要机器学习算法的组合分类器。实验结果表明，车联网设备的识别准确率和召回率分别达到了96.7%和95.2%。设计了一种探测方案，通过选择TCP重传消息的多组特征，获取TCP重传包，增加传统TCP指纹的粒度空间。然后，根据集成学习的 bagging 策略，生成了具有五种主要机器学习算法的组合分类器。实验结果表明，车联网设备的识别准确率和召回率分别达到了96.7%和95.2%。