Device identification (DI) is one way to secure a network of Internet of Things (IoT) devices, whereby devices identified as suspicious can subsequently be isolated from a network. In this study, we present a machine-learning-based method, IoTDevID, that recognizes devices through the characteristics of their network packets. As a result of using a rigorous feature analysis and selection process, our study offers a generalizable and realistic approach to modeling device behavior, achieving high predictive accuracy across two public data sets. The model’s underlying feature set is shown to be more predictive than existing feature sets used for DI and is shown to generalize to data unseen during the feature selection process. Unlike most existing approaches to IoT DI, IoTDevID is able to detect devices using non-IP and low-energy protocols.

中文翻译：

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IoTDevID：一种基于行为的物联网设备识别方法


设备识别 (DI) 是保护物联网 (IoT) 设备网络的一种方法，识别为可疑的设备随后可以与网络隔离。在本研究中，我们提出了一种基于机器学习的方法 IoTDevID，该方法通过网络数据包的特征来识别设备。由于使用了严格的特征分析和选择过程，我们的研究提供了一种通用且现实的方法来建模设备行为，从而在两个公共数据集上实现高预测准确性。该模型的基础特征集比用于 DI 的现有特征集更具预测性，并且可以泛化到特征选择过程中未见过的数据。与大多数现有的 IoT DI 方法不同，IoTDevID 能够检测使用非 IP 和低能耗协议的设备。