Intelligent device identification based on traffic time-series features
Senhao Zhu, Xiaofeng Lu · 2023
With the rapid proliferation of Internet of Things (IoT) devices, the security risks associated with IoT devices are increasing rapidly. It is necessary to identify various types of intelligent IoT devices. This paper proposes an algorithm based on time series features to group and merge data packets and calculate statistical features to identify IoT devices. The protocol features in the response message header of IoT devices are taken into account. Furthermore, with the feature selection technique of maximum information coefficient and information gain, important feature subsets are selected by considering both the network properties and security properties of the features. Finally, based on machine learning algorithms, traffic features of smart devices are modeled to achieve identification of different IoT devices. The experimental results show that the proposed method has good adaptability and achieved higher identification accuracy in two different public datasets.