Identifying IoT Devices Based on Spatial and Temporal Features from Network Traffic

Table 1

Summary of related works.

References	Features	Method
[14–17]	Clock skew	—
[18–21]	Radio frequency fingerprint	—
[22]	Clock skew	ANNs
[23]	Features from the packet head	Twofold identification technique (Random Forest + Edit Distance)
[24]	Flow-level network traffic and knowledge of servers run by the manufacturers	—
[25]	Periodic communication traffic features	KNN
[26]	Features from DNS queries and HTTP URI’s	Improved k-means algorithm, Random Forest, SDN
[27]	Statistical attributes such as activity cycles, port numbers, signaling patterns, and cipher suites	A multistage machine learning (Naive Bayes + Random Forest)
[28]	18 features of DHCP	Dirichlet process
[29]	Features from passively received broadcast and multicast packets	Multiview wide and deep learning framework
[30]	Features in TCP header per TCP flow	PCA, Random Forest
[4]	Raw network traffic from devices	CNN, BiLSTM
[31]	Banners, honeypots	Active scanning
[34–36]	Banners	Active scanning, match
[37]	Banners	Active scanning, search and match