  In this paper, we investigate the domain names of servers on the Internet
that are accessed by IoT devices performing machine-to-machine communications.
Using machine learning, we classify between them and domain names of servers
contacted by other types of devices. By surveying past studies that used
testbeds with real-world devices and using lists of top visited websites, we
construct lists of domain names of both types of servers. We study the
statistical properties of the domain name lists and train six machine learning
models to perform the classification. The word embedding technique we use to
get the real-value representation of the domain names is Word2vec. Among the
models we train, Random Forest achieves the highest performance in classifying
the domain names, yielding the highest accuracy, precision, recall, and F1
score. Our work offers novel insights to IoT, potentially informing protocol
design and aiding in network security and performance monitoring.
