The emergence of the Internet of Things (IoT) concept as a new direction of technological development raises new problems such as valid and timely identification of such devices, security vulnerabilities that can be exploited for malicious activities, and management of such devices. The communication of IoT devices generates traffic that has specific features and differences with respect to conventional devices. This research seeks to analyze the possibilities of applying such features for classifying devices, regardless of their functionality or purpose. This kind of classification is necessary for a dynamic and heterogeneous environment, such as a smart home where the number and types of devices grow daily. This research uses a total of 41 IoT devices. The logistic regression method enhanced by the concept of supervised machine learning (logitboost) was used for developing a classification model. Multiclass classification model was developed using 13 network traffic features generated by IoT devices. Research has shown that it is possible to classify devices into four previously defined classes with high performances and accuracy (99.79%) based on the traffic flow features of such devices. Model performance measures such as precision, F-measure, True Positive Ratio, False Positive Ratio and Kappa coefficient all show high results (0.997–0.999, 0.997–0.999, 0.997–0.999, 0–0.001 and 0.9973, respectively). Such a developed model can have its application as a foundation for monitoring and managing solutions of large and heterogeneous IoT environments such as Industrial IoT, smart home, and similar.

物联网（IoT）概念作为技术发展的新方向的出现引发了新问题，例如有效及时地识别此类设备，可用于恶意活动的安全漏洞以及此类设备的管理。物联网设备的通信产生的流量具有与常规设备相比的特定功能和差异。这项研究旨在分析将此类功能应用于设备分类的可能性，而不论其功能或用途如何。对于动态且异构的环境（例如设备数量和类型每天都在增长的智能家居），这种分类是必需的。这项研究总共使用了41种IoT设备。通过监督机器学习（logitboost）概念增强的逻辑回归方法用于开发分类模型。多类分类模型是使用物联网设备生成的13种网络流量功能开发的。研究表明，根据此类设备的流量特征，可以将这些设备分为高性能和准确性较高的四个预先定义的类别（99.79％）。模型性能度量（例如精度，F度量，真实正比，错误正比和Kappa系数）均显示出较高的结果（分别为0.997-0.999、0.997-0.999、0.997-0.999、0-0.001和0.9973）。这样开发的模型可以将其应用作为监视和管理大型和异构IoT环境（例如工业IoT，智能家居等）解决方案的基础。