Ensemble machine learning approach for classification of IoT devices in smart home

TLDR

The Internet of Things has introduced new challenges such as device identification, security vulnerabilities, and traffic characteristics distinct from conventional devices. This study investigates whether traffic flow features can be used to classify IoT devices regardless of function. Using 41 IoT devices, the authors built a multiclass classifier with 13 traffic features, applying logistic regression enhanced by LogitBoost. The model achieved 99.79 % accuracy with precision, F‑measure, and Kappa values near 0.998, demonstrating its suitability for monitoring heterogeneous IoT environments.

Abstract

Abstract 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.